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Transport Equity for All

Griffith merit based scholarship aiming to build knowledge of disability-informed transport equity and sector capacity by developing with transport planners and people with disabilities, resources to promote and apply equity in transport planning.

Resolving patterns and origins of species richness on the World’s largest tropical island

Griffith merit based scholarship investigating patterns of vertebrate species richness in New Guinea, with emphasis on drivers of biotic interchange with northern ɫ����վ

Numerical modelling to assess the carbon sequestration potential of phytoplankton blooms triggered by artificial upwelling

Industry aligned scholarship focusing on the carbon sequestration potential of artificial upwelling in the ocean based on numerical modelling, advanced data analytics and artificial intelligence (AI).

A novel transdisciplinary framework for equitable governance of genetic resources

Griffith merit based scholarship to develop and test a novel transdisciplinary governance and legal framework for research and development (R&D) on genetic resources

Mitigating the dark side of AI-powered virtual influencers

Griffith merit based/grant funded scholarships examining how the realism of virtual influencers influence perceptions and behaviours and how AI-generated virtual influencers affect young consumers’ body image, self-perception, and mental health.

Breaking the Cycle: Understanding Precarity in ɫ����վ’s Youth Workforce

Griffith merit-based scholarship investigating the mechanisms through which precarious employment creates ripple effects on young people's broader development and adjustment during the pivotal stages of late adolescence and early adulthood

Harnessing Public Mis/Trust

Griffith univeristy merit-based scholarship mapping and studying public trust and mistrust in institutions. The primary focus of this role is to conceptualise, collect, analyse and write up data contributing to potential responses to contemporary challenges of public trust.

Ensemble data-driven spatio-temporal sewer pipe network asset condition assessment system

Grant funded scholarship is one out of a total 11 PhD scholarships awarded to Griffth University (Lead) and James Cook University through CSIRO’s Next Generation Emerging Technologies Graduate Program. The primary focus of this role is carrying out impactful research in civil engineering, environmental and construction applications incorporating advanced data analytics and artificial intelligence (AI)

Biorefining of Agricultural Biomass into Low-Cost Carbon Materials for Sodium Ion Battery Applications

Grant funded scholarship investigating the development of a novel carbonisation approach for lignin modification to achieve high electrochemical performance for energy storage applications

Advancing Health Equity with the Tracking Cube

Griffith merit based scholarship to explore and evaluate how culturally-grounded, decision-support tools like the Tracking Cube can promote health equity and improve early identification and support for neurodevelopmental concerns in Aboriginal and Torres Strait Islander children

Artificial Intelligence-Powered Diagnosis of Skin Cancer: Advancing Early Detection and Personalised Treatment

Griffith merit based/grant funded scholarship investigating the development of advanced privacy-preserving Artificial Intelligence (AI) technologies for the early diagnosis and management of skin cancer

Market-based political activism

Grant funded scholarship examining civil society activism focused on market-based actors and processes

Enhanced performance of flexible road pavement design incorporating wicking geotextile and machine learning

Industry aligned scholarship evaluating the performance of wicking geotextile for flexible pavement design, aligning with ɫ����վn Standards and AASHTO guidelines.

Anthropocene in ɫ����վ: faunal change through time

Grant funded scholarship investigating the nature and direction of changes in ɫ����վn fauna in response to natural and anthropogenic factors

Anthropocene in ɫ����վ: faunal communities

Grant funded scholarship investigating the nature and direction of changes in fossil mammal communities in response to natural and anthropogenic factors

Spinal Cord Injury Rehabilitation [BioSpine Project]

Griffith merit based scholarships focusing on the development of non-invasive rehabilitation technology to recover sensorimotor function in people with spinal cord injury

Human Foot Biomechanics

Griffith merit based scholarship investigating the modelling and augmenting of human foot function to improve musculoskeletal health and physical performance across the lifespan

Stretchable microfluidics for improved fluid and particle handling

Grant funded scholarship investigating the generation of new knowledge in advanced manufacturing of flexible devices and miniaturised prototypes for biomedical disease diagnosis, therapeutic agent production, and fisheries management

Voice and Belonging: Pathways to inclusion for new migrant communities through media engagement

Grant funded scholarship investigating the role of ɫ����վ's ethnic media in the humanitarian and refugee settlement experience, conceptualising media engagement as a key lens through which to foster a sense of belonging

Bacterial subversion of metal ion intoxication

Griffith merit-based scholarship investigating the molecular mechanisms used by pathogenic streptococci to avoid killing by metal stress

MAIC Griffith University Road Safety Research Collaboration

Grant funded scholarship examining the fields of traffic psychology and work driving behaviours

Fragment-based drug design against neurodegeneration

Griffith merit based scholarship investigating the development fragment-derived compounds for the treatment of neurodegeneration

Virtual environments in the Metaverse provide almost infinite visual real estate for interacting with 3D data visualisations. This provides opportunities for novel deployment of large data sets across diverse domains, including health, climate, education, defence, cybersecurity, blockchain, etc. However, there are significant challenges to useful data engagement in the Metaverse where information and users may be distributed across environments but still need to collaborate. This project will explore the visualisation of 3D data using immersive head-mounted displays that support eXtended Reality (XR) and develop new paradigms for virtual and real-world data interaction into, within and out of the Metaverse.

Crop yield estimation plays a significant role in management of agricultural activities and decision making, such as fertiliser (e.g., nitrogen) use, crop insurance, harvesting and storage requirements, and budgeting. Visual yield assessments by growers or agronomists could be highly subjective and labour intensive. Methods based on satellite RGB image along with existing agronomic and meteorological data compute in-season vegetation indices often estimate yield for an entire district or region, so they lack in farm-specific yield estimation. Moreover, sugarcane yield estimation does not depend much on the 2D spectral information (i.e., leaf-colour), rather on the plant height and stalk density. Thus, the project will investigate the use of 3D point (aerial laser scanning) data for accurate sugarcane yield estimation. These data will provide important cues to estimate number and density of sugarcane stalks.

Globally, the whale watching industry has been increasing in size and economic value since the 1990s. Whale-watching tourism has transformed entire local communities and contributed significantly to economies. The whale-watching industry, and the whales themselves, face uncertain threats from multiple pressures. This includes the impacts of increased sea surface temperature, altered currents and changes in food abundance on whale behaviour. The research project will look at adaptations of the whale watch industry to changing whale distributions and abundance, drawing from two primary species for ɫ����վn waters for which the Whales & Climate Program has data on climate change impacts. This study involves modelling, social and economic science, with a focus on sustainable tourism.

With the evolution of modern information technology, medical data sharing has become a part of our daily life, which greatly benefits medical research and development professionals, service providers and the society as a whole. The rapidly growing volume and variety of medical data in the past decade has made privacy protection an increasing concern to data owners, obstructing medical data sharing to reach its expected scientific and market value. This project will focus on a solution to provide an adaptive protection for medical data so that data users can create the explainable intelligence based on the shared data without disclosing any privacy information.

This project aims to produce value-added functional 2D nanomaterials by advancing the green, scalable and costeffective electrochemical production method developed by the candidate. In addition to developing transformational electrochemical engineering technology to utilise ɫ����վn raw resources, this project will generate new knowledge in the area of materials chemistry and innovative additive manufacturing technology. Expected outcomes of this project include improved pilot-scale electrochemical reactors for producing various functional 2D nanomaterials and enabling precise control of their molecular and bulk properties. These tailored 2D nanomaterials will significantly improve the performances of flexible and energy-related devices.

Modern Intrusion Detection Systems (IDSs) rely on machine learning for detecting and defending cyber-attacks in information technology (IT) networks. However, the introduction of such systems has introduced an additional attack dimension; the trained IDS models may also be subject to attacks. The act of deploying attacks towards machine learning-based systems is known as Adversarial Machine Learning (AML). The aim is to exploit the weaknesses of the pre-trained model which has “blind spots” between data points it has seen during training. More specifically, by automatically introducing slight perturbations to the unseen data points the model may cross a decision boundary and classify the data as a different class. As a result, the model’s effectiveness can be reduced as it is presented with unseen data points that it cannot associate target values to, subsequently increasing the number of misclassifications. Adversarial machine learning attacks and automated detection of these attacks in computer networks will be investigated in this project. This project aims to investigate adversarial attacks to machine learning in cybersecurity of cyber-physical systems and propose mitigation techniques to defend against these attacks.

Modern Intrusion Detection Systems (IDSs) play a vital role in safeguarding information technology (IT) networks against cyber-attacks. IDSs rely on machine learning techniques to analyse network traffic and identify suspicious patterns and anomalies that may indicate an ongoing or impending attack. However, the deployment of these machine learning-based systems has introduced an additional vulnerability, as the models they use to detect and respond to threats may also be subject to attacks. This type of attack is known as Adversarial Machine Learning (AML) and involves exploiting the underlying machine learning algorithms to evade detection, misclassify data, or manipulate the training process. This project will study AML techniques in cybersecurity domain and propose defence strategies.

Air route development (ARD) is a well-known business process within airports and airlines. Airports are usually perceived as leading partners in this stakeholder engagement, aiming to attract new and retain existing airline partners. Still, multiple other stakeholders are involved in the process, and their support or lack of it significantly impacts the sustainability of a particular route. This project aims to propose new ARD feasibility calculation methods that airports, airlines, and other involved stakeholders could use as decision-making tools. Commercial air routes are the primary focus of this project, with the potential to extend it to cargo routes.

This project aims to recover all the genetic information from four ancient humans. Two of these iconic specimens come from ɫ����վ and two from Malaysia. We will sequence the entire DNA (genomes) and proteins (proteome) of Mungo Man (Willandra), as well as the Yidinji King (Cairns), the Deep Skull (Borneo) and the Bewah specimen (Malaysian Peninsula). This will provide a better understanding of the settlement of ɫ����վ and new knowledge about the ancient people of Australasia and their relationship to other human populations worldwide. The research will use cutting-edge methods of DNA and protein sequencing of ancient human material and will provide critical reference genomes / proteomes that will anchor future research.

Antimicrobial therapies have been a magic bullet against infectious diseases since their introduction. However, due to the excessive use of antibiotics via irrelevant and unregulated access, the efficacy of the antibiotic has declined rapidly in parallel with increases in antibiotic resistant bacterial strains. Resistance to this antibiotic has risen rapidly and its clinical usefulness has declined to a point that it is now rarely considered a frontline treatment option. With the emergence of resistant Gram-negative ‘superbugs’, infections caused by multidrug-resistant Gram-negative bacteria have been named as one of the most urgent global health issues due to the lack of effective drugs. Numerous research for new antibiotics focus on developing improved versions of existing molecules, Amongst these new designed and engineered drugs, nanosized particles have gained much recent attention due to their physical size, biocompatibility and functionalities. Nanoparticles are expected to provide a localized cure for complex diseases by facilitating targeted delivery and improved bioavailability. The functionalized nanoparticles can either act as the vehicle for potent drugs or they themselves can act as the therapeutic agents. We have developed antibiotic conjugated carbon-based nanoparticle systems and the conjugated systems displayed notable antibiotic effects on various gram-negative bacteria, including those resistant to the antibiotic moiety conjugated onto the nanoparticle. The aim of this project is to extend these systems to include different antibiotic moieties to construct a range of effective antibiotic conjugate analogues onto the nanoparticles.

Respiratory infections and antibiotic resistant bacterial infection are some of the conditions that have significantly stressed our hospital ICU. A number of risk factors have been reported to associate with severe diseases, which includes age, pre-existing conditions, pathogen setpoints, responsiveness to therapeutic strategy. Any single risk factor is unlikely to be an absolute determinate of clinical diseases, rather many contributors or associations are important with the disease progress. We will use ICU data and artificial intelligence to generate a prediction algorithm to assist clinical decision making.

In both real and simulated environments, people can be overwhelmed if exposed to high levels of competing stimulus which can negatively impact cognitive load. When using immersive technologies, for example for augmented reality, there are opportunities to add useful virtual objects into a real-world environment both as objects located in 3D space and as part of an extended user interface (UI), i.e., a head-up display (HUD). These elements need to be managed as not to diminish user experiences.

Help us save the sea turtles! Chemical contaminants are accumulating in marine wildlife worldwide. However, due to their large size and often protected status, there are ethical and logistical constraints in conducting traditional whole animal toxicity tests on these animals. Recently, cell-based bioassays have been proposed as an ethical alternative to assessing the effects of contaminants in marine megafauna. This project aims to establish marine wildlife cell cultures and develop species-specific cell-based toxicity bioassays to assess the effects of chemical pollutants in marine wildlife. This project will involve both field and lab components, and include collaborations with state and federal government agencies, non-profit conservation organisations and the private sector

The current industrial-scale hydrogen productions are reliant on high temperature steam reforming fossil fuels, consuming large quantity of energy and fossil resources, and emitting huge amounts of CO2. This project aims to develop cheap and plentiful transition metal-based high performance water splitting electrocatalysts, enabling economically viable large-scale water electrolytic hydrogen production driven by renewable electricity. A theory-guided catalyst approach will be used to guide the efficient design and development of high performance electrocatalysts. The success of the project will lead to a suit of high performance water splitting electrocatalysts, leaping forward water electrolytic hydrogen production technology.

This project aims to develop novel stream learning algorithms for continuous patient outcome prognosis by taking into account patient's data collected during ICU admission in a unified manner. The algorithms are expected to integrate high frequency time series data with patient's demographic data, lab data, diagnosis data, prescription data, etc. as exemplified in MIMIC-III, for accurate outcome prognosis. Issues such as prediction bias, data leakage, data sparsity, non-stationarity, model explainability will be investigated.

Advanced cyberattacks pursue their victims over months or years until they can reach their final goal. Detecting these threats in early phases before the final stage of attacks can be executed against endpoint devices can help prevent adversaries from achieving their goals. Many organisations use cyber threat hunting to proactively detect hidden intrusions before they cause a major breach. The goal of hunting is detecting threat actors early in the cyber kill chain by searching for signs of an intrusion and then, providing hunting strategies for future use. An emerging method in cyber threat hunting is using Natural Language Processing (NLP) methods to automate the hunting process. This project aims to investigate and develop practical threat hunting approaches using NLP methods. This method can be used to automate the extraction of indicators of compromise (IoCs).

There is currently a surge in interest in marine and coastal restoration, with a significant number of projects underway, and many more planned. Current methods for monitoring restoration progress and success vary enormously, with low uptake of technological advances that promote efficiency and comprehensiveness. This project will work towards a coordinated, open-science approach to monitoring, that standardises data formats, allows trade-offs or synergies between ecological, socio-economic and cultural benefits to be explored, and facilitates cross-project comparisons and benchmarking. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams.

There is currently a surge in interest in marine and coastal restoration, with a significant number of projects underway, and many more planned. Current methods for monitoring restoration progress and success vary enormously, with low uptake of technological advances that promote efficiency and comprehensiveness. This project will work towards a coordinated, open-science approach to monitoring, that standardises data formats, allows trade-offs or synergies between ecological, socio-economic and cultural benefits to be explored, and facilitates cross-project comparisons and benchmarking. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams.

Cyber threat intelligence (CTI) refers to knowledge about potential threats, which includes information on threat indicators such as Tactics, Techniques, and Procedures (TTPs), IPs, and more. CTI can help organisations identify existing threats, either through external open-source threat intelligence or by monitoring adversarial activities within their own networks. The generated CTI can be used to build intelligence about threats against a specific target. Initially, indicators of compromise (IoCs) are generated, and these IoCs can be processed and shared using CTI sharing techniques. Such techniques allow security analysts to use CTI information from other companies and share their IoCs with trusted partners, which can be used to update detection rules and blacklists in security devices such as IDSs and firewalls. To enable effective and collaborative cyber threat intelligence sharing, the application of state-of-the-art machine learning techniques in the CTI generation and sharing should be investigated. This project will review automation of CTI generation and sharing using machine learning . The efficacy of using machine learning technology for detecting network attacks has been widely studied, but it has been difficult to create an ML-based detection system that can handle diverse network data samples from different organisations. This project aims to propose an automated cyber threat intelligence sharing using machine learning that enables multiple organisations to work together to share their IoCs.

The future of scientific advancement will certainly involve a mixture of computational prediction and experiment. This interdisciplinary research theme promises to develop next generation theories for better modelling of chemistry, using novel mathematical and physical models. Suits students with a strong interest in applied mathematics and computers. Machine learning techniques will likely feature in this project.

Biochar is a solid by-product of thermochemical conversion of biomass (in the absence or reduction of oxygen) to bio-oil and syngas, which is dominantly composed of aromatic compounds resistant to biological degradation. Biochar would enhance soil aeration, increase soil pH, favour nitrogen immobilization, interact with available organic C and N in soil, act as an electron shuttle for soil microorganisms and modify soil enzyme activities as well as microbial abundance and community composition. This project aims to investigate how modification of pyrolysis process (i.e., pyrolysis temperature; heating rate; residence time) and co-pyrolysis of biosolid with organic wastes (i.e., feedstock type; blending ratio) would reduce the environmental risks associated with biosolid (i.e., heavy metals; microplastics; PAHs; PFAS), while improve its quality (i.e., C content, specific surface area; porous structure; water holding capacity) for application in agricultural systems.

Blue-green algal blooms dominate many ɫ����վn lakes and reservoirs. Toxic species create major problems for drinking water and recreation. We work collaboratively with environmental and water managers to determine the factors controlling these blooms with both field and lab work.

As restoration projects gain traction during this Decade for Ecosystem Restoration, we need to develop techniques that secure the success of these projects and achieve expected outcomes. The project works with many stakeholders including Traditional Owners, farmers, State Government and local council to determine whether ecosystem services, including nutrient retention, carbon sequestration and biodiversity, develop within current wetland restoration projects.

Our research has found that leaves from trees leach organic matter that can negatively effect algae. However, at the catchment level it is unclear how much impact the organic matter from trees is having on algal blooms. This research would involve working with the water industry to tackle this question.

Electric power systems are considered critical infrastructure and are susceptible to various contingencies such as natural disasters, system errors, and malicious attacks. These contingencies can have a severe impact on the world's economy and cause significant inconvenience to our daily lives. Hence, the security of power systems has been a topic of considerable interest for decades. With the recent development of the Internet of Things (IoT), power systems can support various network functions throughout the generation, transmission, distribution, and consumption of energy through IoT devices like sensors and smart meters. However, this has also led to an increase in security threats. Cascading failures are one of the most severe problems in power systems and can result in catastrophic impacts such as widespread blackouts. Furthermore, these failures can be exploited by malicious attackers to launch physical or cyber attacks on the power system. This project aims to investigate cascading failure attacks and develop AI techniques to detect and defend against them. Feel free to contact me for further discussion.

Seawater is the most abundant aqueous resource on earth that is readily accessible at very low costs, but yet to be directly utilised for production of hydrogen fuel and commodity chemicals. This project aims to develop cheap and plentiful carbon-based high performance chlorine evolution electrocatalysts for seawater electrolysis powered by renewable electricity to realise the production of hydrogen, chlorine and sodium hydroxide directly from seawater. The electrolyser can also be used to treat desalination brine while produce hydrogen and chemicals. The success of the project will set a firm technological foundation for seawater utilisation, which will add to ɫ����վn capability to meet future energy and environment challenges.

Computational science and engineering is a modern approach to research, distinct from standard theoretical and experimental approaches. In computational research, fast computers are used to simulate or model the behaviour of physical systems to better understanding properties too difficult or expensive to study via experiments. Nano- and micro-scaled systems can pose a particular challenge for conventional experiments and theory, and are a natural fit for computational study.

Macular degeneration causes devastating visual impairment in the ɫ����վn population, and without new and effective treatment options, one in seven ɫ����վns with early signs of macular degeneration will likely progress towards advanced stages of the disease. The earliest known pathophysiological event to occur in macular degeneration is choroidal vascular dropout, and little is known about the events that occur prior to this microvasculature dysfunction and the contribution of the surrounding choroidal stroma and its resident cell populations. This project aims to construct a multicellular bioengineered choroid to elucidate deeper understanding about this specialized sensory support tissue.

Continuous beam atom interferometers for quantum enhanced navigation

Atom interferometers have demonstrated great promise for next generation accelerometers and gyroscopes, with significant gains in sensitivity and immunity to bias drift . To date, most work has focused on pulsed atom interferometers, which use a series of time-seaparated light pulses to split and recombine the atomic ensemble, with the resulting phase shif. However, pulsed approaches suffer from significant loss in bandwidth, due to dead-time where no measurement is made. This project will construct a continuous beam interferometer using laser cooled rubidium atoms, with the interfereterometer sequence constructed by atoms traversing spatially separated light fields, giving significant gains in bandwidth and flux.

Coordinated action by multiple agents (such in robotic swarms), is an important area, especially whe there is limited or only intermittent communication. This requires both local planning and adjustment when there is a possibility to coordinate, so that the swarm as an emergent agent can fulfill an overall intent. The work would involve literature review, theory building, and validation through simulation experiments (using a multi-agent simulation platform).

Quantum state smoothing is a newly developed way to estimate the state of a quantum system at time t using measurement results in both the past and future of t, with applications in experiments with continuous measurements. This project will further develop this formalism, including using it to address the question of what is the most likely thing a quantum system would have done if you had measured it in a different way from how you did. Feel free to contact me about other areas I have published in recently.

Human activities introduce a huge diversity of pollutants into the environment, often with harmful consequences for wildlife. These pollutants frequently overlap, but many knowledge gaps exist when it comes to predicting their combined risks. Light pollution and pharmaceutical anti-depressants are two of the fasting growing stressors globally and have both been shown to negatively impact aquatic animals, but there has been no research exploring their interactive effects. This project aims to investigate the combined risks of light pollution and anti-depressant pharmaceuticals on the regulation of circadian systems, at multiple levels of biological organisation. The outcomes are expected to yield a new framework for exploring the interactive effects of chemical and non-chemical stressors and to reveal how non-chemical circadian entrainment cues such as light pollution modulate chemical toxicity.

As the use of connected devices and the Internet of Things (IoT) becomes more prevalent in manufacturing processes in the Fifth Industrial Revolution (Industry 5.0), cybersecurity becomes a critical consideration. The integration of these devices presents new opportunities for cybercriminals to exploit vulnerabilities and attack the system, hence organisations must implement robust cybersecurity measures to safeguard their data and systems. A significant cybersecurity challenge in Industry 5.0 is protecting the data generated by connected devices. This information is often confidential and valuable, and unauthorised access to it can disrupt operations or result in intellectual property theft. To counter this risk, it is crucial to encrypt, securely transfer and store data to prevent unauthorised access. The project will study the new threat landscape in industry 5.0, and propose mitigation strategies for the new vulnerabilities introduced by the Fifth Industrial Revolution.

Many companies have the privacy policy set for the data they collected. Due to the evolution of AI-based technology, how AI shall be used to help with an automated privacy impact assessment?

This project proposes to develop advanced AI models capable of providing deep insights into complex interdependencies and heterogeneous behaviors observed across multiple data views. By leveraging state-of-the-art deep learning techniques and interpretability methods, the project seeks to unravel intricate relationships and patterns within multi-view data sources, enabling a comprehensive understanding and explanation of how diverse factors interact and contribute to observed behaviors. Through rigorous experimentation and analysis, the project aims to enhance the transparency and interpretability of AI models, facilitating informed decision-making in various domains such as healthcare, finance, and social sciences.

The 3D object reconstruction is highly challenging due to high data complexity, structural variations, presence of noise, and missing of data. Buildings come in different shapes and their unique architectural designs pose a great challenge, specifically for extraction and modelling of small components such as chimneys. The recent deep learning architectures have shown high success in object-part segmentation, e.g., a plane can be divided into wing, body, fin, and stabilizer. So, prior knowledge about a building roof style can be sought as a prerequisite step for building roof reconstruction. The project aims to employ deep learning architecture to segment a roof into parts and then classify them into roof styles before the roof is reconstructed as a complex shape.

Microplastics (MPs) are a major emerging contaminant in agroecosystems, due to their significant resistance to degradation in terrestrial environments. This project asseses the characteristics and fate of MPs in contaminated soils and their risks to soil biota.

This project aims to develop non-wetting droplets that can be used for chemical reactions and cell culture. These non-wetting droplets, known as liquid marbles, act as standalone miniature reactors that can be manipulated by external stimuli. This project focuses on using liquid marbles as building blocks for an integrated digital reactor platform that substantially improves reaction rates. You will work with research or commercial users to explore novel solutions as well as design and build innovative devices.

Elevated levels of terrigenous sediments in river systems has long been regarded as one of the most deteriorating factors on water quality in rivers and coastal area. However, the land use sources of sediments in rivers systems are uncertain. In this study we will develope novel biogeochemical fingerprinting models for tracing the terrestrial source of sediment and nutrient in river systems.

This project aims to exploit high-performance, durable and cost-effective defective electrocatalysts for fuel cell and water splitting applications. It expects to generate a new area of knowledge to understand the interfacial phenomena of electrocatalysis and of how to develop technologies for the controllable synthesis of low-cost and highly efficient electrocatalysts. The expected outcomes of this project include a process for the development of cost-effective electrocatalysts thereby making hydrogen fuel cells and water splitting techniques economically competitive.

Machine learning is one of the hottest topics in computer science, but it is often used as a "black box"; consequently, the trained model may behave unexpectedly and yield catastrophic results. This project aims to develop new methods to understand machine learning models. We will adopt various techniques that synthesise different forms of "explanations" as approximations of the original machine learning models. We will evaluate these methods for a variety of machine learning algorithms, including CNN, RNN, random forest, and reinforcement learning. As a case study, we will look into modelling the abstract state machines in Process Analysis Toolkit (PAT) and develop an interactive query system that allows the user to ask questions about machine learning models and get answers. We will also investigate how to present the interactive system in a user-friendly interface.

We are currently looking for a PhD candidate to work on Soil Ameliorants. The primary purpose of this role is to develop a series of novel Soil Ameliorants from locally available materials or wastes. The ideal candidate needs to have a relevant background in chemistry. Success in this role requires collaboration with research partners, industry and farmers. This PhD project will be based on Nathan Campus, Griffith University.

Bioretention systems are excavated basins or trenches that are filled with porous filter media and planted with vegetation to remove pollutants from stormwater runoff.The main aim of this project is to examine the impacts of locally available recycled organic amendments on improvement of plants performance and reduction of nutrient leaching from bioretention filter media. The main objective is to design a cost-effective and functional bioretention filter media with optimum nutrient retention capacity and carbon storage for supporting sustainable plant performance in bioretention systems.

Vascular calcification is an actively-regulated process mediated by vascular smooth muscle cells where calcium phosphate crystallizes in the form of apatite, predominately depositing in the vascular tissues. Vascular calcification is one of the predictors of cardiovascular disease and can lead to cardiovascular dysfunction. This project aims to develop novel biomimetic-functional nanomaterials for targeted treatment of vascular calcification. This intelligent material is expected to specifically reach VC site where it releases a local anti-calcification activity, which could minimise off-target side effect and enhance therapeutic capability with minimal administered dose.

Macroalgae or seaweeds are a fundamental component of the Great Barrier Reef, but their diversity is poorly known. This project aims at discovering and documenting the diversity of marine benthic algae using molecular methods for a better understanding of their natural history and roles in coral reefs.

Drinking water supply is fundamentally influenced by climate. As climate change occurs, potentially causing longer duration of droughts and more frequent storm events, it is essential to assess how it will affect our drinking water security. This project will use recent updates to climate change datasets and hydrological models to assess drinking water security across ɫ����վ

Connectivity is a guiding principle for conservation planning, but due to challenges in quantifying connectivity, empirical data remain scarce. This project provides solutions to the challenge by using computer vision to automatically extract fish movement data from underwater camera streams. The student will develop expertise in fisheries ecology, statistical modelling and programming. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams. It will lead to better planning and management of marine restoration and protected area projects through incorporation of connectivity principles.

Connectivity is a guiding principle for conservation planning, but due to challenges in quantifying connectivity, empirical data remain scarce. This project provides solutions to the challenge by using computer vision to automatically extract fish movement data from underwater camera streams. The student will develop expertise in fisheries ecology, statistical modelling and programming. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams. It will lead to better planning and management of marine restoration and protected area projects through incorporation of connectivity principles.

Coral reefs are complex ecosystems but are under threat from anthropogenic activities. When reefs degrade, corals are normally replaced by macroalgae, therefore understanding macroalgal ecology is critical for the conservation of the Great Barrier Reef (GBR). This project aims at providing fundamental knowledge of the ecological processes involved in macroalgal blooms in the GBR.

Electroencephalography, or EEG for short, is a technique that measures the electrical activity of the brain through electrodes placed on the scalp. This non-invasive and cost-effective technique has been used in various fields, including neuroscience research and clinical practice. One of the main reasons why EEG research is so important is because it allows us to gain valuable insight into brain function and dysfunction. It has been extensively used to investigate a wide range of cognitive processes, such as attention, perception, memory, language, and emotion. Furthermore, EEG has been instrumental in diagnosing and monitoring neurological disorders, including epilepsy, sleep disorders, and traumatic brain injury. Overall, EEG research is an essential tool for understanding the human brain and its disorders. This project focuses on research on EEG biometrics and its applications.

Changes in fire regime and global warming are significant and interactive symptoms of climate change. In this study we would like to investigate the long-term, interactive impacts of fire and warming on soil C dynamics and soil-to-atmosphere C fluxes in different ecosystems

This project focuses on understanding how nanostructures affect electrochemical reactions. More specifically, this project aims to understand how electrochemistry in nanoconfinement affects Li ion transport to improve the performance of Li-batteries

This project aims to design and develop functional nanomaterials and nanocomposites for high-performance wearable energy storage devices (WESDs). A functional materials approach, together with precise control of device architecture through multi-materials/techniques additive manufacturing will be used to achieve maximum device performance with the required mechanical properties. The expected outcomes of this project include a detailed understanding of materials and devices structural-property relationship and the establishment of the fundamental principles on the microfabrication of flexible energy storage devices to support the burgeoning field of wearable devices, thus advancing the field of materials chemistry and advanced manufacturing.

High risk industries reliance on procedures is high; there are checklists, memory items, procedures, manuals and rules that direct how a cockpit should be configured, what to do in an emergency and whether an aircraft can take off given the physical environmental conditions. Despite their relevance, the number of procedures and rules is increasing every year without a direct translation into a reduction in the number of accidents and incidents. As an alternative to the current approach to procedures, which are seen as the only way to create safety, resources for action see procedures as a supplement to the activity. It provides the information required to complete a task if and when the worker needs it. However, how do procedures as resources for action look like in practice? In this research project, we aim to develop normal and abnormal situations checklists sensible to the context that provide the information needed, when needed, if needed.

This project aims to engineer a highly versatile micropatterned surface that can be used to culture and study cells.

This project aims to develop highly efficient and stable semitransparent perovskite solar cells for innovative smart solar windows. The key concept is to explore novel functionalisation strategies on emerging carbon and two-dimensional materials to fabricate semitransparent perovskite solar cells for self-powered smart photovoltaic windows. Expected outcomes of this project include not only placing ɫ����վ at the forefront of research in the fields of materials science and renewable energy, but also creating commercial opportunities in ɫ����վ. This project expects to have various benefits for ɫ����վns – through the development of a cutting-edge sustainable energy device and the establishment of strong international collaborations.

The progress made in fields such as the internet of things, artificial intelligence, machine learning, and data analytics has facilitated the development of digital twin technology. A digital twin is a high-fidelity digital model of a physical asset or system that can be utilised to optimise operations and predict faults of the physical system. Operators of cyber-physical systems need to be aware of the cyber situation in order to adequately address any cyber attacks in a timely manner. Early detection of cyber threats can quicken the incident response process and mitigate the consequences of attacks. However, gaining a complete understanding of the cyber situation may be difficult due to the complexity of cyber-physical systems and the ever-changing threat landscape. More specifically, cyber-physical systems (CPSs) usually have to be continuously operational, and they may be sensitive to active scanning of the network traffic. Digital twins can address these challenges by providing virtual replicas of physical systems that can be analysed in-depth without disrupting operational technology services. This project aims to assess the usefulness of digital twins for the cybersecurity of cyberphysical systems and review the tools and technologies available for creating them. Additionally, a cybersecurity framework for anomaly detection using digital twins in cyber-physical systems will be proposed.

When metals absorb atomic hydrogen from molecular hydrogen gas, first a disordered solid solution and then an ordered concentrated hydride phase are formed, with evolution of heat (enthalpy). The entropy and enthalpy changes are fundamentally linked through statistical mechanics. The goal of this PhD project is to control the enthalpy of the hydridring reaction by controlling the entropy change, with relevance to hydrogen storage (low enthalpy is desirable) and metal-hydride hydrogen compressors (high enthalpy is desirable). This would desirably involve both theoretical modelling using Density Functional Theory/Calphad to explore the possibilities of designing alloys such that the solid-solution phase has significantly higher/lower configurational entropy than the concentrated hydride phase, and experiments to make small amounts of alloys and measuring their hydrogen uptake properties in the National Hydrogen Materials Reference Facility within QMNC).

The project will investigate the fate and effects of firefighting chemicals and bushfire leachates in Eastern ɫ����վn waterways to assess the risk they pose to aquatic organisms and ecosystems on the short term and long term. Firefighting chemicals are deployed by emergency services for the protection of life and property, however there is a gap in the knowledge associated with their short- and long-term effects to water quality and aquatic ecosystems. This will be a largely lab-based experimental project and will aim to better understand if and at what scale these chemicals impact aquatic ecosystems and the timescales associated with these potential impacts. Other lines of evidence will also be explored such as the identification of ‘signatures’ associated with firefighting chemicals to better understand the contribution they have to water quality impacts in a large severely burnt catchment. This project is a collaboration between the NSW Government’s Estuaries and Catchment team based in Lidcombe NSW and Griffith University (Gold Coast campus) with opportunities to work across each location. Focus areas are bushfire-related aquatic ecotoxicology, environmental pollution, and environmental chemistry.

this project aims to improve our understanding of how ecosystem processes affect soil carbon quality and quantity, and how this in turn influences soil resilience to environmental stresses (e.g. drought, compaction, chemical residues of fungicides, and carbon decline) and to develop sensitive and affordable assessment protocols for improvement of soil carbon stocks and functional resilience to environmental stresses.

Pre-existing research has failed to offer a solution to protect patients’ privacy and confidentiality, it is important to identify the limitations of existing solutions and envision directions for future research in privacy preservation in health informatics. This research aims to identify current outstanding issues that act as impediments to the successful implementation of privacy measures in health informatics and the limitations of available solutions. Feasibility of using blockchains for dealing with health and medical will be researched and evaluated. Then, propose a privacy-preserving framework by improving data storage, record linkage techniques.

Gullies are the majority source of sediment discharged into the Great Barrier Reef, motivating significant investment to prevent erosion and improve water quality in receiving environments. Large amphitheatre gullies are complex structures with highly variable erosion processes. Process-based models are required to inform rehabilitation practices, and to inform investment at the catchment scale. This project will develop models of gully erosion suitable for informing management in ampitheatre gullies. This project will involve collaboration with Queensland Government and the Queensland Water Modelling Network and is associated with an ARC Industry Fellowship.

Over the past few years, many intrusion detection systems (IDSs) have been developed using machine learning methods. These automated IDSs can automatically analyse network data, including network traffic and device logs, to detect intrusions. Cybersecurity experts rely on these systems' recommendations to improve network security. To enhance the reliability of IDSs, it is important that the decisions made by these machine learning-based solutions can be justified to humans. However, the current automated IDSs are used as black boxes, providing no information about the reasons behind their predictions. It should be clear to cybersecurity experts which features of the network data caused the intrusion. This project aims to identify state-of-the-art techniques to develop an explainable IDS, addressing this gap and providing a better explanation of IDS decisions. It will investigate how existing methods can be improved to provide more comprehensive interpretability of machine-learning-based IDSs and provide details about the features involved in IDS decisions.

Essential powerline components such as conductor, cross arm and insulator will be periodically extracted and their properties (narrowing of diameters, broken discs, sizes, material fatigue) will be estimated using machine learning techniques combined with a statistical analysis. Also, faults in these components will be automatically detected and managed. Along with point cloud data, multispectral, hyperspectral as well as thermal imagery could be used for these purposes.

This project aims to develop an advanced system for automatically identifying and flagging fake news articles using large language models. Leveraging the capabilities of large language models like GPT, this project involves preprocessing a diverse dataset of news articles, extracting meaningful features, and training a classifier to distinguish between genuine and fake news. This project will explore fine-tuning techniques to enhance the model's adaptability to different domains and evolving forms of fake news. The ultimate goal is to deploy a robust fake news detection system capable of assisting in the ongoing battle against misinformation and safeguarding the integrity of online information dissemination.

In today’s digital ecosystem world, we see more and more intelligent devices are connected over the Internet, enabling them to share their data on the Web. This allowed us to collect a large amount of data and use then for intelligent situation awareness and intrusion detection. This research will address the key issues facing to cyber security: 1) big streaming data analysis, 2) the huge number of generated alarms with the vast majority being false alarms, 3) human effort to investigate alarms to find intrusions, 4) determination and removal of false alarms, 5) timely decision making in a constantly changing environment, and 6) the ability to capture previously unknown attacks.

Graph data are ubiquitous nowadays. Real-world graphs (e.g., social network graphs, knowledge graphs, road networks) are getting larger and larger, which makes many common graph queries (e.g., subgraph matching/counting, crucial nodes/edges identification, cohesive subgraph computation, constrained shortest paths) time-consuming. However, in many real-world applications, approximate answers are sufficient and much easier to find. This PhD project aims at developing novel techniques for the fast finding of approximate answers, focusing on subgraph computation/counting queries.

Developing and applying nanotechnologies to deliver solutions to forensic problems. Broadly speaking, these activities seek to apply materials science in a forensic context. Key areas of focus include: new fingermark development strategies; improving the specificity of presumptive testing for drugs of abuse; and assessing new and overlooked classes of evidence. Key themes include: safer, greener forensic processes; delivering new functionality or clearer interpretation; and interdisciplinary, practitioner-informed research.

The popularity of OpenAI ChatGPT revolutionised the GenerativeAI. While these models provide huge benefits training such models is time-consuming and costly, which causes the information in such models to be not up to date. Proposed methods to finetune smaller base LLM with up-to-date data do not necessarily discard irrelevant outdated data from Large Language Models. This work will look into options to ensure finetuning of the models forgets only desired parts and does not cause catastrophic forgetting due to multiple model fine-tuning.

This project investigates the semantics of popular programming languages at different levels. For instance, we will formally model the semantics of a high-level programming language, such as rust, in a formal verification tool, such as a theorem prover, to understand exactly how a piece of code works and whether it is correct with respect to the user specifications. We will also model program semantics at a lower level (e.g., compiled binary code) and check whether low-level semantics conforms with high-level code. The formal modelling of the programming language of choice should lead to verification tools with practical impact in the industry.

The current wave of deep learning and AI research has yielded many advances in how tools such as neural networks, optimization, or uncertainty quantification can be used to improve modelling capability for a number of useful applications. Projects are available in the development of robust and transparent machine learning and AI techniques that can be employed to augment existing computational modelling techniques (e.g. surrogate models, reduced order models, etc) or to provide new avenues of solution (e.g. PINNs as a famous example).

This Project aims to investigate the mechanism that integrates local search and complete search, and machine learning for real-world applications. This project will develop the strategies for the cooperation of local search and complete search in solving hard problems from real-world. It will explore the cooperation of local search and complete search for training deep neural networks. On the other hand, this project will propose novel mechanism to design local strategy to by using machine learning technologies. The aims of this Project include both the novel paradigm for training deep neural networks and efficient algorithms with the cooperation of local and complete search strategies.

Life is the dynamics of large biomolecules. This project aims to develop a novel experimental approach to achieve atomic levels of control over large biomolecules through manipulation of electrostatically levitated bioparticles in a Paul trap. This starts with single yeast cells and will progress to developing laser and electron optics techniques to controllably fragment the cell into organelles and into isolating single chromosomes. These chromosomes will then be controllably and potentially reversibly unfolded using single electron changes in the static electrical charge to demonstrate an atomically resolve force microscopy. A Gold Coast based joint with the Institute for Glycomics

Three-quarters of the periodic table is metals, and essentially all of these can be made to absorb hydrogen to form "alloys" (e.g. PdH) and compounds (e.g. MgH2). Thousands of metallic alloys also absorb hydrogen. In very many cases, hydrogen first forms a dilute solid solution which, as the hydrogen gas pressure increases, becomes unstable and a phase transformation to a concentrated hydride phase occurs, up to the thermodynamic critical point of the system. The goal of this PhD project is to investigate some new ideas about hydrogen uptake by metals. Two of these are: (i) Recent theoretical work based on Density Functional Theory proposes that in nanoparticles the system can transform via a single phase even below the critical point. This published result is controversial and has not been proved experimentally. This proposed phenomenon will be investigated in the Pd - D2 system by measuring hydrogen uptake while performing neutron diffraction (at the ɫ����վn Neutron Scattering Centre in Sydney) to determine what phases are present. (ii) Recent analysis based on statistical mechanics predicts that the assumed linear relationship between log(absorption pressure) and reciprocal temperature (the van 't Hoff relation) is in fact curved at high pressure, which matters for applications such as hetal-hydride hydrogen compressors for vehicle filling stations. This published result will be tested by measurements of hydrogen uptake by alloys at pressures up to 2000 atmospheres.

Current AI/ML techniques are limited in terms lacking meta-cognition that allows a system to reason about its own abilities and capabilities in light of the problem space encountered. The project would suit a candidate who is interested in both the theory of AI and in experimenting with implementation tools to build efficient and effective AI systems (e.g. SOAR, CLARION, ACT-R,...).

The aviation industry is often seen as a symbol of globalisation, connecting people and businesses worldwide. However, despite its global reach, the industry has been slow to address issues of gender inequality. Women have been historically underrepresented in aviation, from academia to industry boards. This has led to a lack of diversity in leadership positions and a culture that can be unwelcoming to women. In recent years, there has been a growing recognition of the importance of gender equality in aviation. From initiatives to increase the number of women in pilot training programs to campaigns to promote diversity in leadership roles, the industry is taking steps to create a more inclusive environment. Despite the progress that has been made, there is still a long way to go to achieve gender equality in aviation. By continuing to push for change and challenging the status quo, this research aims to explore avenues that will lead to a more inclusive future for aviation.

Cyber threat intelligence (CTI) is the knowledge about a threat, and it includes threat indicators such as Tactics, Techniques, and Procedures (TTPs), IPs, etc. CTI can help organizations to learn about existing threats. Cyber threat intelligence can be received from external open-source threat intelligence, or it can be extracted from adversarial activities in organizations’ networks. The CTI generated will be used to build intelligence about threats against a given target. In cyber threat intelligence, indicators of compromises (IoCs) are generated. These IoCs of the detected adversary can be processed and distributed. Security analysts to use CTI information from other companies and share back their IoCs with other trusted partners. These shared IoCs can be used to update detection rules and blacklists in security devices like firewalls. This project will review state-of-the-art techniques CTI sharing and identify gaps in the current solutions. It will also investigate how threat intelligence can be automatically created and shared for new emerging attack and link the CTI to cyber security defence mechanisms.

We are currently looking for a PhD candidate to develop genomic resources and tools for ɫ����վn papayas to facilitate future smart breeding of elite varieties. The primary objectives of this role are to: (a) sequence and annotate the reference genomes of selected ɫ����վn papaya varieties and (b) develop high-density genetic markers for ɫ����վn papaya and wider germplasm collections. The goal is to then uncover genomic sequences that may be used for accurate selection of preferred flavour and productivity traits across a broad germplasm set. Outputs from this project will directly contribute to genomic prediction approaches for developing elite papaya varieties. This project includes molecular, genomic and transcriptomic approaches that will leverage prior knowledge and skills developed in our group and by collaborators. These include high density SNP mapping and QTLs underpinning several fruit quality traits and possible gene candidates, as well as trained sensory panel and biochemical profiling performed to identify volatiles and other compounds that are associated with distinct fruit flavours. Success in this role requires collaboration with fellow team members and leading researchers from the University of Queensland, Murdoch University and the Queensland Department of Agriculture and Fisheries.

ɫ����վ's expansive coastal and marine ecosystems are in dire need of improved biological monitoring to preserve their valuable and unique biodiversity in the face of human-related disturbances. This Project responds to the challenge by upscaling and revolutionising fit-for-purpose genetic toolkits that can extract whole ecosystem DNA data from environmental samples. This innovation will be done in the interest of answering previously inaccessible ecological questions related to biodiversity, supporting habitat restoration and engineering solutions, complementing rather than replacing existing biological monitoring, supporting commercial outcomes with automation, and benchmarking the health of coastal and marine ecosystems under threat.

ɫ����վ is home to large reserves of "critical minerals" - those metals that are essential to the transition to renewable technologies. Our knowledge of the environmental chemistry of these metals is currently limited, particularly in the coastal and marine waters that will likely be their ultimate sink. This project seeks to use advanced analytical approaches, including Synchrotron-radiation X-ray spectroscopy, to unravel the complex aquatic geochemistry of critical metals in coastal and marine environments.

Graph neural networks (GNNs) are emerging techniques for AI. As many chemical compounds and proteins in biology can be modelled as graphs, GNNs have great potentials for drug discovery. This research will investigate new GNN based techniques to accelerate the process of drug discovery.

Through previous work with Prof Bernhard Moller and Turing Award Laureate Sir Tony Hoare, we proposed a geometric theory for program analysis in which a computer program is represented by dots and lines in a diagram. Prof Moller has laid out the theoretical foundation of the work, and we are now ready to proceed into a more practical development. Our vision is to build a program analysis tool with Graphical User Interface (GUI) that supports writing and modelling programs by drawing diagrams and automatically translating a diagram into an executable program. A diagram can also be converted to a Communicating Sequential Process (CSP) model in our tool Process Analysis Toolkit (PAT) and be used for model checking. The outcome is a toolchain that supports user-friendly program analysis, testing and verification.

Wetlands can accumulate large amounts of carbon, but when disturbed, this carbon can be released into the atmosphere as CO2 and CH4, contributing to global warming. This project aims to determine how disturbances, including hydrological modifications, feral animals and deforestation, affect the carbon cycle of wetlands (mangroves, marshes and supratidal forests) and how can these be reversed.

Today's room-temperature superconductors are all metallic hydrides. Superconducting transition temperatures (Tc) now reaching, even exceeding, room temperature are however observed only under extreme pressure, above 1 million atmospheres. Palladium hydrides - PdH, PdD and PdT - have been known to superconduct below about 10 kelvin for 50 years. It was recently found that PdH and PdD can become superconducting after absorbing hydrogen at a temperature and pressure above the thermodynamic critical point of the Pd - H2/D2 system, with superconducting transition temperatures reliably reaching 60 kelvin in PdDx (where x is not yet known). While this is low compared to room temperature, only low hydrogen pressures are required. The goal of this PhD project is to conduct cryogenic experiments to measure Tc of PdDx by by means of resistivity and alternating susceptibility simultaneously, under various experimental conditions, and to thereby understand how to obtain the highest possible Tc for this system.

Double emulsions, referring to droplets of the disperse phase containing even smaller ones, are highly desirable for applications in drug delivery, food science, release of substances, etc., due to the embedded structure which can encapsulate different types of molecules. This project aims to produce and control high-throughput double emulsions using microfluidics.

Hyperspectral videos contain rich spectral, spatial and temporal information of moving objects. The goal of this project is to develop fundamental hyperspectral image analysis, object detection and tracking methods and explore their applications in agricultural, environmenal, and medical applications.

The project will assess the impacts of bushfires on water quality and biogeochemical processes within Eastern ɫ����վn waterways to better understand the short- and long-term impacts of bushfires on aquatic biogeochemical cycles in estuaries. The fate, transport, and cycling of target metals and nutrients (Fe, Mn, C, N, P, S) will be the focus of this study with both laboratory and field-based experiments utilised. This project is a collaboration between the NSW Government’s Estuaries and Catchment team based in Lidcombe NSW and Griffith University (Gold Coast campus) with opportunities to be based at either location. Focus areas are bushfire-related aquatic biogeochemistry, environmental pollution, and environmental chemistry.

Wildlife are in peril due to numerous threatening processes. Amphibians (especially frogs) are the most endangered Class of Veterbrates. Two key threats to amphibians are disease and environmental contaminants. The main disesae of concern is the fungal disease chytridiomycosis (pronounced 'ki-tri-di-o-my-co-sis'). This disease is caused by two related fungi: Batrachochytrium dendrobatidis and B. salamandrivorans. It is the most devastating disease threat to biodiversity ever recorded. To date it has caused the decline and/or extinction of hundreds of frog species around the world. Another key threat to amphibians (and other aquatic fauna) are environmental contaminants (including pesticides, heavy metals, firefighting chemicals, etc.). I have PhD opportunities available to study (1) infection dynamics of chytridiomycosis in frogs, exploring mechanisms of resistance and tolerance to the disease; and (2) independent and interactive effects of multiple threats to frogs, including the disease chytridiomycosis, and environmental contaminants.

While supervised learning is known for a while introduction of pretrained transformers reduced the demand and therefore the cost for annotation of training data. Pretrained transformers learn from large amounts of unlabeled text data and are a form of Large Language Models (LLM). Another milestone in AI was the introduction of the Generative Pretrained Transformers (GPT) framework, which has a decoder layer and is able to understand and generate human-like text. The popularity of OpenAI ChatGPT revolutionised the GenerativeAI. While these models provide huge benefits training such models is time-consuming and costly, which causes the information in such models to be not up to date. For example, popular ChatGPT3.5 was trained with data generated prior to January 2022. There are approaches to finetune smaller base LLM with domain-specific data, however, further improvements are needed to improve accuracy, reduce hallucination and ensure information generated from such models is up-to-date.

Many real-world problems have multiple, conflicting objectives and/or constraints that are dynamic in nature. These problems are reffered to as dynamic multi-objective optimisation problems. Nature-inspired population-based algorithms enable natural parallel search for a set of optimal trade-off solutions. This study will investigate how a decision maker's preferences can be incorporated in the dynamic search, to focus the search around more preferred regions of the optimal trade-off solution set in an interactive and dynamic way. The algorithms will be applied to disaster management and recovery.

This project is an investigation into the time that it takes for an electron to tunnel-ionise from molecules, referenced to the tunnelling time from atomic hydrogen The proposed research is based around a state-of-the-art laser system, the ɫ����վn Attosecond Science Facility (AASF). This laser system is unique in ɫ����վ and one of only a few around the world. The light pulses generated by this laser are highly amplified and are only a few cycles of the optical field, and so measured in attoseconds (10-18 sec). We study of the interaction of such strong-field engineered light pulses with matter. The research will build on a ground-breaking research into the time it takes for tunnel ionisation to occur in atomic hydrogen, which was recently published by the Griffith team in Nature [Sainadh et al. Nature, 568, 75 (2019). This project will extend the measurement of the tunnel ionisation of electrons from other atoms and molecules and will provide the most stringent tests to current models for these interactions.

Blockchain is a promising technology towards achieving full-scale digital transformation in a complex environment. This technique has attracted a number of successful applications such as cryptocurrency, supply chain, trade finance and smart contracts. Blockchain has been showcased as a game changing national technological strategy in several countries. This project will extend our current work on the classification of digital assets, cross-chain integration protocols, and formal verification of smart contracts with novel design patterns and formal security guarantees for inter-blockchain systems. Experiments and validation will be carried out on test-networks and using real-world case studies with our industry collaborators

Due to the “black box” characteristics of the deep learning technique, the deep network-based computer-aided diagnosis systems have encountered many difficulties in practical application in healthcare. The crux of the problem is that these models should be explainable – the model should give doctors rationales that can explain the diagnosis.The objective of this project is to research on highly interpretable algorithms to generate "trust" between the human users and the algorithm, designing user-friendly explanations and developing comprehensive evaluation metrics to further advance the research of interpretable machine learning in biomedical image analysis.

We are offering two Ph.D scholarships for motivated students to work on patterns of species richness and turnover across Australasia, with emphasis on drivers of biotic interchange between Northern ɫ����վ and New Guinea. Depending on background and interests, students will have research, travel/fieldwork funds to support work on projects such as (a) diversity and systematics of key groups of frogs or lizards, or (b) broadscale projects on patterns and processes of biotic turnover, quantification of biodiversity hotspots, and implications for conservation.

Nutrient offsetting provides a market based mechanism for restoring catchments to improve the water quality in rivers and the coasts. Point source polluters pay to restore non-point source pollution in catchments. However, there are significant gaps in knowledge in comparing point and non point sources of nutrients in terms of how they affect the environment. This project will work collaboratively with industry and government to examine these nutrient sources and link them to nutrient responses in the environment.

This project focuses on developing explainable AI solutions to decision support systems, by combining knowledge graphs, machine reasoning and machine learning. Knowledge graphs is a promising data and knowledge organisation, synthesis and management approach, and we have developed scalable reasoning tools for knowledge graphs coupled with ontological rules that describe domain knowledge or business rules. This project aims to study the problem of incorporating such high-level knowledge and formal reasoning in the analysis of cross-media data. Moreover, such knowledge and reasoning can be integrated with machine learning models to provide powerful support for informed decision-making where a justification or explanation of the decision can potentially be retrieved.

Knowledge graphs are important tools to enable next generation AI through providing explanation for different applications such as question answering. Knowledge graphs are typically sparse, noisy, and incomplete. Knowledge graph reasoning aims to solving this problem by reasoning missing facts from the large scale knowledge base. This project aims to develop novel scalable technique for knowledge graph reasoning. The developed techniques will be further generalised to more general graphs with graph neural networks.

AI-based human face recognition is a mature technology and has been adopted in many applications, such as mobile phone. However, recognition of animal face is still an under-investigated topic. Leveraging the success in human face recognition technology, this project aims to develop novel koala face recognition methods based on transfer learning using images and videos captured in the natural environment. The research outcome will leave to innovative tools for koala population estimation and conservation.

Land-based run-off is one of the greatest threats to marine ecosystems, following climate change. Marine restoration efforts are ramping up due to global initiatives and local success stories. While restoration is needed, it is also crucial to understand and elimate threats that degraded land and seascapes to begin with. This project will assess the potential risk of land-based run-off of marine restoration and prioritise areas to focus future efforts.

Land-based run-off is one of the greatest threats to marine ecosystems, following climate change. However, it is largely ignored in international agreements. Those that do aim to address the issue largley focus on plastics and nutrients but often ignore sediments. This project will explore how international conservation agreements can be better leveraged to reduce all aspects land-based run-off.

Work with an interdisciplinary team to study how aquaculture and windfarming will interact with ɫ����վ’s marine ecosystems. Focal areas include marine spatial planning of aquaculture and windfarming and cumulative effects assessments.

This project will examine the unique 3.5 million year old megafauna fossils from Chinchilla Rifle Range, Queensland. The project will focus on the taphonomy of the site, and the sequence of fossils collected in systematically excavated sites. Several unusual fossils are awaiting description and taxonomic identification, and palaeoenvironmental proxies revealing ancient ɫ����վn habitats can be further interogated.

This project focuses on multidisciplinary research at the interface between chemistry, nanotechnology, biology and medicine. The primary goal of our research is to advance the diagnosis and treatment of life-threatening diseases such as cardiovascular diseases, cancers and blood disorders with the help of nanotechnology and microfluidics.

Micro- and nano-plastic debris in aquatic, terrestrial and marine habitats have become significant concern for human health. Due to their tiny size, developing a high-throughput system to detect and classify them is a challenging task. It has been proven that shortwave hyperspectral imaging technology is highly effective in classifying plastics in the size of tens of micrometers. Nevertheless, when the size of plastics reduces to sub-micrometer or nanometers, traditional hyperspectral microscopic system becomes infeasible. This project aims to develop an innovative technology for micro/nano-plastic detection and classification using dark-field hyperspectral microscopy. The scope of the project includes hyperspectral image capture with dark-field microscopy, image processing, and machine learning method development for particle detection and classification. The student will work with a multidisciplinary team in ICT, Environment, Material Science, and Mechanics. 

Microgrids provide a flexible architecture for deploying distributed energy resources that can meet the wide range needs of different communities from metropolitan cities to rural country areas. This project aims to develop new control and optimisation technologies to implement self-scheduling and self-coordinating among all microgrids in a networked microgrid. It provides a feasible solution for the challenge of both the growing number of microgrids and high penetration level of renewable energy in a power grid. The outcomes of this project will promote the increase in the renewable energy fraction of the total electricity supply in ɫ����վ and worldwide.

Microplastics have been widely found in various environments including water, sediment, soil, biota and air. There is growing conern that human can be exposed to microplastics through consumption of microplastic contaminated water and food. This project aim to analyse microplastics in various foods and beverages and assess the human diatary exposure to microplastics and associated health effects

This project aims to investigate microplasic contamination of agricultural soils and their fate and impacts on soil and plants as well as potential toxic effects to human via consumption of microplasic contaminated crops

The transition to a global energy economy based on renewables is extremely urgent and underway. Hydrogen energy technology has a very imprtant role to play. For instance, it is estimated that the electric power required to produce and export enough hydrogen to satisfy just Japan's needs is more than 1 terrawatt. This compares to ɫ����վ's National Electricity Market which peaks at about 30 gigawatts, but also to our readily accessible resource of offshore wind at more than 2 terrawatts.

Trapped ions are a powerful tool for the analysis of charged bioparticles and biomolecules. Paul traps are used for high-resolution, long-duration confinement in this application. However Paul traps have selective stability depending on trap parameters and particle properties. This project would model the impact of permanent and induced electrical dipole moments on the theoretical stability of ion trajectories in a Paul trap and related the limits of electrical confinement of a charge point particle to an electric dipole in an optical tweezers.

Microfluidics is both the science that studies the behaviour of fluids through microchannels and the technology of manufacturing microminiaturized devices containing chambers and tunnels where fluids flow or are confined. The previous works use rigid materials to construct the devices, and the device's functionality is mainly based on single physics. This project will design and develop a cutting-edge microfluidic technology by exploiting multiple physical coupling and flexible materials to achieve a variety of functions such as micropumps, micromixers, cell sorters, trappers etc. This technology will be applied in the lab-on-a-chip system for disease diagnosis, prognosis and treatment.

Nanobubble technologies have applications in wastewater treatment, surface cleaning, sanitization, and therapeutics towards some age-associated diseases. This project focus on fundamentals of nanobubbles by exploring the stability, nucleation and dynamics of nanobubbles. The project aims to develop technologies to generate nanobubbles and apply the gained nanobubble technologies in agriculture to treat biomass, in poultry and dairy to achieve fast promotion of animal growth, in aquaculture to increase productivity and feed-conversion ratio with short harvesting cycle, in catalyst chemistry for renewable energy, and in therapeutics to treat diseases

Exosomes are nanoscale (≈30–150 nm) extracellular vesicles of endocytic origin that are shed by most types of cells and circulate in bodily fluids. Exosomes carry a specific composition of proteins, lipids, RNA, and DNA and can work as cargo to transfer this information to recipient cells. Recent studies on exosomes have shown that they play an important role in various biological processes, such as intercellular signaling, coagulation, inflammation, and cellular homeostasis. These functional roles are attributed to their ability to transfer RNA, proteins, enzymes, and lipids, thereby affecting the physiological and pathological conditions in various diseases, including cancer and neurodegenerative, infectious, and autoimmune diseases (e.g., cancer initiation, progression, and metastasis). Due to their unique composition, easy accessibility and capability of representing their parental cells, exosomes, and exosome containing RNAs, proteins draw much attention as promising biomarkers for screening, diagnosis and prognosis of these diseases via non-invasive or minimally invasive procedures. Therefore, isolation and analysis of tumor-derived exosomes and exosome containing biomarkers at the early stage of the diseases could significantly improve the capacity to diagnose the diseases thereby improving outcomes. The Shiddiky group is pursuing studies of the development of multifunctional magnetic nanomaterials based technologies and devices for the highly selective isolation and sensitive detection of exosome and exosomal biomarkers (mRNA, proteins) in patients with cancer and other disease.

Nanoparticles have a great potential to be used in water treatment due to its high surface area. This can be utilised efficiently for removing toxic metal ions, microbes and organic matter from water. However, due to their sizes, nanoparticles often form aggregates/agglomerates lowering their activities. To prevent these, further processing including surface passivation is applied. The use of activated carbon into the nanoparticle systems is another strategy as it is simple and economical. Activated carbon was recorded to be used for a multitude of applications, including water filtration/treatment, gas phase adsorption and decolourising agents in the food industry. Research into improving both structure and applications has grown exponentially in recent decades as environmental sustainability has become a key focus, especially the areas involved in environmental remediation. Combination of the nanoparticle and activated carbon provides an excellent platform for the environmental applications such as enhanced capacities and rates.

Complex formal proofs require significant effort and tools such as interactive theorem provers, whereas automated reasoners are often limited by the size of the problem or computational time. Recent advancements in machine learning have led to new tools, such as Sledgehammer, that use traditional theorem provers in smart ways to improve run time and automation. New algorithms such as HyperTree Proof Search applies ideas from Monte-Carlo Tree Search and deep reinforcement learning to push the boundaries of state-of-the-art theorem proving. This project aims to adopt the above ideas to develop new ML-based tools for Isabelle/HOL that can reason about logical formulae faster and more automatically.

One of the greatest challenges to airline flight safety, is how well the pilots diagnose and respond to complex abnormal or emergency situations, such as multiple failures, false alarms, and inoperative systems, which can arise during flight of modern aircraft. Aircraft have evolved to become technically highly sophisticated, becoming more ‘robot than machine’, but approaches to pilot support in the modern cockpit lag well behind. Pilot support in the cockpit remains limited to traditional approaches (the checklists and procedures of the Quick Reference Handbook), effectively leaving pilots unsupported, 'mostly on their own’ to deal with complex, safety critical situations. The aim of the research is to harness areas such as AI/reasoning/machine learning alongside research in Human Computer Interaction to develop new approaches to supporting pilot decision making, thereby enabling pilots to diagnose and respond more safely and effectively to the complex abnormal or emergency situations that they will encounter when flying modern aircraft.

This project will seek to develop new approaches for determining stone tool function. Emphasis will be placed on experimental and quatitative methodologies, with application to key questions about early human adaptation to new and changing environments.

The use of 2D fingerprint technology has become widespread in various authentication applications, such as mobile phones, laptops, and building access control. However, this technology has limitations, as it cannot fully replicate a real finger and is vulnerable to spoofing attacks. As a result, there has been a shift towards the development of 3D fingerprint biometrics, which offers benefits such as hygienic, contactless, anti-spoof, and natural representation. The aim of this project is to explore 3D fingerprint biometrics and to develop an AI-guided 3D fingerprint biometric system. The project will involve the application of AI/deep learning on 3D point cloud data. Feel free to contact me for further discussion.

This project centres around construction of simulation frameworks for a variety of high impact plasma and electron transport applications, such as atmospheric lightning discharges, low temperature plasma-solid interactions through to magnetically confined fusion plasmas. Areas of investigation can be tailored to candidate expertise & interests, including numerical solution techniques for transport equations, the closure problem, machine learning and AI in computational science, kinetic or Monte Carlo methods.

Plant pathogens reduce global crop productivity by up to 40% per annum, causing enormous economic loss and potential environmental effects from chemical management practices. Thus, early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control and management are crucial. Detecting and quantifying pathogen species and their relevant genetic biomarkers in plant extracts at the early stages of the diseases is notoriously difficult to access via conventional methodologies. This is mainly because they are either too slow to enable efficient intervention and application of fungicides (visual observation of symptoms in the field) or are too expensive and technically complex to be used by non-specialized technicians on an industrial scale. The development of an affordable, sensitive, specific, user-friendly, rapid and equipment-free method for broad-scale disease surveillance in crop plants, based on “on-farm” pathogen detection and quantification, is of great interest to the agricultural industry and plant biology. The Shiddiky Laboratory focus to develop portable devices and technologies for ‘on-farm’ analysis of pathogen species and pathogenic biomarkers in unprocessed plant extracts. Such a device would allow more rapid and cost-effective detection, control and management of the plant diseases.

As the number of advanced cyber attacks is rapidly increasing in the modern world, it is crucial to detect attacks as soon as possible to prevent them from reaching their final goal and causing destructive damage. Therefore, a robust cybersecurity system is needed to detect and respond to potential cyber-attacks in a timely manner. Although automated intrusion detection using artificial intelligence has been proposed by many researchers, the performance of these methods still needs improvement. This project aims to review the applications of optimisation algorithms, such as the whale optimization algorithm (WOA), in improving the performance of networks (ANN)-based solution to detect cyber-attacks. Different optimisation algorithms will be analysed and compared to find the method that can outperform other methods.

Traditional control techniques have limitations when it comes to ensuring that a swarm of autonomous agents (whether fully or partially automated) fulfill their tasks, while at the same time observing the rules of engagement. The project will explore the possibilities of command rather than control over such multi-agent systems. (The project suits someone who is eligible to work for ɫ����վn Defence).

Data streams are sequences of data that are continuously transmitted to a receiver. Outlier detection is to identify abnormal data, or data that are significantly different from normal data. The problem of detecting outliers from data streams has important applications and has attracted a lot of attention from researchers. However, there are still many challenges in accurately and efficiently identifying outliers, that is, how to effectively distinguish normal data from outliers, and how to achieve real-time identification. This PhD project aims to develop novel techniques for the problem.

We are developing microfabricated silicon nitride based photonic waveguides to interface with rubidium atoms as a platform for realising quantum devices. The first device in this project aims to demonstrate a wavelength converter from the 780 nm light used in atomic magnetometry to the long-distance telecom compatible 1529 nm light. This is an experimental physics project which includes fibre optics, photonics design work, microfabrication, atomic physics, and vacuum systems with the goal of advancing towards manufacturable devices.

Aquaculture is one of the fastest growing food sectors in the world, with great potential for expansion. Climate change poses a significant threat to aquaculture production - from potential losses in infrastructure to sub-optimal growth and production rates, but climate change is rarely included in aquaculture development plans. In this project you will work with an interdisciplinary team to assess and incorporate climate risk into aquaculture planning to future proof aquaculture production under a changing climate.

A variety of projects are available in different modelling areas, with the focus applied to modelling a variety of important physics scenarios important to tokamak plasmas, such as those anticipated in ITER. Equilibrium plasma discharge, tokamak disruption, runaway electrons, edge-plasma, and surface wall interaction applications are examples of focus applications.

The development of an affordable, sensitive, specific, user-friendly, rapid and equipment-free diagnostic method that can detect diseases at the time and place of patient care (i.e., point-of-care) using minimal specialised infrastructure, has the potential to transform health care to many millions people both in the developed and developing countries. Recent advances in sequencing and proteomics technologies have now given rise to a large number of potentially useful genetic, epigenetic and other novel molecular biomarkers for the development of diagnostic methods for many diseases including cancer, infectious and neurodegenerative diseases. Despite these great input from biomedical engineering, significant technical challenges for achieving a functional POC device are yet to be overcome. This is mainly due to the lack of sensitive, specific, rapid and low-cost readout methods. The Shiddiky group is pursuing studies of improving existing and developing entirely new methods that can rapidly detect cancer, infectious and neurodegenerative diseases.

Despite the tremendous efforts in developing effective on-site biosecurity and best management practices, waterborne parasites still cause significant health and economic burden worldwide. Early and rapid diagnosis together with an understanding of disease severity is critical for preventing parasite spread and enabling effective management strategies. Current routine diagnostic tests for waterborne parasites are not suitable for on-site detection. Shiddiky Laboratory is working on developing novel biosensing platform devices for the quantification and genotyping of waterborne parasites in surface and recreational waters. The device can be used to ensure improved waterborne parasite management, risk prediction, and rapid mitigation of impending outbreaks.

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their privacy leakage. Notably, instances such as ChatGPT inadvertently revealing its training data through deliberately designed prompts underscore the pressing need to address privacy vulnerabilities in DNNs. This proposal seeks to delve into the vulnerabilities of DNNs to privacy attacks, examining potential threats stemming from learning paradigms, model architectures, training data, training processes and inference outputs. By understanding these risks, the project aims to develop robust privacy-preserving mechanisms and effective defences against privacy leakage, ensuring that the deployment of DNNs aligns with stringent privacy standards.

AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.

The Broadwater on the Gold Coast is a large semi-enclosed tidal estuary that forms the southern portion of Moreton Bay. While the estuary receives fluvial sediments from four major river catchments, the dynamic coastal processes have long dominated sediment inflow into the estuary. The study seeks to quantify the ratio of catchment-derived to marine-derived sediments in the estuary, and determine refined proactive management mechanisms for maintaining ecosystem function and navigability within the estuary.

Aquaculture is a fast growing industry in ɫ����վ - which is known for production safe and relatively sustainable seafood products. ɫ����վ has great potential for aquaculture expansion but currently has limited knowledge of how aquaculture impacts the environment now and in the future. This project will work to quantify major environmental impacts from ɫ����վn aquaculture (nutrient pollution, GHG emissions, etc.) and potential impacts on habitat, species and ecosystem services. The work is essential for sustainable ɫ����վn aquaculture and supports many of ɫ����վ's Blue Economy initiatives.

The 2022 Nobel prize in Physics was awarded to the experimental demonstration of quantum entanglement and its counterintuitive properties, in particular the violation of Bell inequalities. A modern way to understand this phenomenon is as a failure of a classical causal model that satisfies relativistic constraints on causal structure. The program of quantum causal models aims at resolving the puzzle of Bell's theorem by extending the classical framework of causality to a quantum setting, while maintaining compatibility with relativistic causal structure. This project will involve further developing the framework of quantum causal models and addressing various open questions, such as counterfactual reasoning, indefinite causal structure, and/or potential applications to quantum information processing tasks.

Quantum technologies are poised to become major drivers of scientific and economic growth in the 21st century. On the other hand, quantum advantage over classical computers has only been demonstrated for a few classes of algorithms. This interdisciplinary project will tackle the key question for unlocking the benefits of quantum information processing: what gives quantum mechanics its information-processing power beyond classical physics? It will explore the hypothesis that quantum advantage is associated to fundamentally different ways in which causality operates in the quantum and classical regimes.

The project will apply quantum machine learning to the problem of tracking the state of an open quantum system. Specifically, we want to find the most memory-efficient classical apparatus, which performs adaptive quantum measurements so as to maintain the state of the quantum system in a stochastically varying conditional pure state. While this problem can be attacked by exact methods in classical numerics, these are very computationally expensive, so machine learning is an obvious alternative. Most interestingly is to use genuine quantum machine learning. That is, to perform quantum machine learning experimentally, where the system itself is part of the machine learning loop. This project thus has an experimental quantum photonics supervisor also.

Despite its enormous scientific and technological success, quantum theory suffers from deeply puzzling conceptual problems, none more vexing than the quantum measurement problem. It involves inconsistencies that arise when considering the treatment of "observers" as physical systems amenable to a quantum description. Recent results on extended versions of the "Wigner's friend paradox" exemplify the measurement problem in the form of rigorous no-go theorems, such as the "Local Friendliness" no-go theorem. It shows that certain sets of a priori plausible assumptions cannot be simultaneously satisfied by any theory that can accommodate certain phenomena where an "observer" can be treated as ordinary systems subject to quantum-mechanical operations. This project, which has both a conceptual and a technical component, aims to propose increasingly convincing experimental realisations of such phenomena, by asking what are sufficient conditions for a system to be deemed an observer, and what experimentally feasible but increasingly sophisticated quantum systems may provide models of quantum-coherent observers.

Software testing can only show the presence of bugs but never their absence, so it is crucial to mathematically prove the correctness of programs in mission-critical domains such as aerospace, defence, finance, health, etc. This practice is called formal (program) verification. The advancements in quantum computing extend the application of formal verification to quantum programs, which is uncharted territory. This project will develop new verification techniques that are suitable for quantum programs, possibly using quantum computing algorithms.

The problem of real-time monitoring of the state of composite structures (such as for example those found in airplanes) requires signal processing and machine learning on the one hand, but extended with logical reasoning that creates explainable decision support.

This project focuses on various methods that are used for the recommender systems based on social networks. Students will explore research issues in recommendation algorithms, and gain experience in applying appropriate methods to predict user preferences in different settings. It is to form the in-depth analysis of data-driven behaviors strongly interdependent with each other. Students will need to propose recommendation solutions for social network users and evaluate the prediction accuracy after applying the methods. Students will explore research issues to design the underlying models and algorithms for those heterogeneous and interdependent behavioral data to make predictions and recommendations, as well as develop software prototypes.

Marine protected areas are the main conservation tool used to address the biodiversity crisis in our oceans. They are also a major focus of international conservation agreements such as the recently adopted Kunming-Montreal Global Biodiversity Framework. This project will use novel methods to quantify human impacts in marine protected areas through time and develop strategies and recommendations to reduce these impacts and improve the effectiveness of marine protected areas.

Nearly 1/3 of coral reefs are threatened by poor qater quality and there are an estimated 800,000 human deaths each year due to sanitaiton-related water pollution. Improved sanitation has the potential to achieve benefits for both nature and people - but is often poorly understood (particularly in communities with little access to resources). This project will asess opportunities for reducing nutrient pollution to achieve both ecosystem and human health objectives - with the potential to incorporate risk and uncertainty from climate impacts.

This research focuses on multidisciplinary research at the interface between chemistry, nanotechnology, biology and medicine. Research at this interface has the potential to generate breakthroughs in fundamental science as well as lead to advanced technologies for diagnosing, monitoring and treating disease. Current (selective) research projects are the following: Point-of-care (POC) diagnostics; microfluidic methods for the detection of cancer; portable devices for cancer epigenetics; nanomachines for exosome and exosomal biomarkers detection; and superparamagnetic materials in biosensing applications.

Safety management systems are a reality and a requirement in many industries, from aviation and healthcare, to oil and gas and constructions. Also known as Occupational Health and Safety (OHS) System, Health, Safety and Enviroment (HSE) Systems, these systems have not been able to improve the safety records as expected and the limitation pointed out by many scholars is the reliance on outdated assumptions and limited evidence. Considering new approaches to safety management, such as safety-II, safety differently, resilience engineering, and other, this research project aims to analyse the limitations of safety management practices commonly employed by safety management sytsems and update or develop new practices. The ultimate goal is to help industry make their SMS more effective.

With the emergence of the Internet of Things (IoT) and Industry 4.0, there is a trend for applying these services and applications in a large-scale industrial area. The IoT paradigm has changed the way of interactions with the things that surround us. In essence, the IoT promises ubiquitous connection to the Internet, turning common objects into connected devices. This project will review the different architectures of IIoT, and systematically study the security challenges associated with interoperability, access control, privacy, and trust-related issues, in general. The project will also identify the gaps in the state-of-the-art security techniques and requirements to determine the security services in the IIoT environment and propose potential mitigation techniques to address these gaps.

Conductors in powerline corridors are thin, so only a small number of laser points get reflected, which makes it difficult to effectively extract the conductors using the aerial point cloud data. We have recent success in extraction of the conductors even when conductors are in bundles of 2 or 4 sub-conductors. However, power line corridors often exist in complex environments (e.g., forests), where occlusions, missing data and noise are regular phenomena. The advancement of recent deep learning techniques will be useful for high-performance powerline corridor segmentation in complex environments.

This project combines artificial intelligence with molecular simulation to develop a predictive understanding of electrolyte solutions for developing the next generation of electrolytes for improved battery technology. Electrolyte solutions are one of the most important substances on earth, playing a central role in energy storage, carbon capture and conversion and essentially all of biology. Until recently, however, we have been unable to predict even their most basic properties. Recent advances in the field of AI and molecular simulation mean that for the first time it is possible to accurately predict their properties with existing software. This project will train students in the exciting and rapidly growing area of artificial intelligence for materials science. Many companies including Microsoft, DeepMind, Google Research and Schrodinger are currently investing in this area.

We have recently developed state-of-the-art techniques for sports video processing using deep learning and strategy and match outcome anlaysis using probabilistic reasoning. This project aims to extend these results to deal with different sports, such as soccer, baseball, basketball, etc. We are also interested in developing applications for match analysis, visualisation, match outcome simulation and so on. The current methods may be combined with large language models to provide smart responses to user queries.

Antimicrobial resistance to commonly prescribed antibiotics remains an ongoing global threat. This project will develop theranostic nanomaterials that overcomes antimicrobial resistance and allows both diagnosis and stimuli-responsive treatment of infectious diseases in one dose. The outcome of the project will open a whole new way to manage and treat infectious diseases.

This project will investigate the archaeology of southern Africa to better understand the origins and evolution of Homo sapiens. The focus will be on the Late Pleistocene record in regions that have been less well-studied (i.e., the deep interior savannah and desert environments).

With the exponential growth of streaming data from various sources in both volume and content, privacy protection for streaming data and their secure analysis are becoming increasingly important. Considering the properties of streaming data including mass volume (unbounded size), heterogeneity, dynamicity, concept drift and feature evolution this project applies multi-fold theories and techniques including secure computing, privacy protection, machine learning, intelligent searching, data mining in an effectively coordinated way. The project first studies how to discover and measure sensitive information of data instances, including features and labels, in data streams. It then investigates suitable models, schemes and mechanisms for effective protection of the sensitive information while preserving the required data utility. Finally it develops new techniques and methods for various privacy-preserving streaming data analysis and mining, including statistical analysis, association mining, classification and clustering, and evaluation of their performance.

Micro- and nanoscale systems exhibit unique properties that can’t be predicted from the theory of large-scale systems. In order to develop new strategic micro- and nanotechnologies, open questions on the behaviour of very small systems need to be addressed. Micro- and nanoscale systems exhibit unique properties that can’t be predicted from the theory of large-scale systems.

Using full-spectrum photographic equipment available at Griffith University, this synthetic project will develop new chemical dyes, stains, and fluorophores, that primarily emit in the UV and IR regions of the electromagnetic spectrum. These emissive treatments will then be trialled and validated against existing forensic treatments.

Cancer is a major cause of illness in ɫ����վ and has a substantial social and economic impact on individuals, families and the community. Although technologies continue to evolve, currently, most cancers could not be completely cured. This project will explore different innovative ways involving thrombosis for more effective treatment of cancers

This project explores the relationship between reinforcement learning (RL) and probabilistic model checking (PMC), as both are built upon the underlying model of Markov decision processes. On the one hand, PMC may be used to guide and constrain an RL agent when exploring optimal solutions so that the agent operates within a "safe region". On the other hand, RL may be used to improve the performance of model checking algorithms through statistical methods. We aim to improve the state-of-the-art of both worlds.

Simulators and training devices are applied in a range of educational settings. From vocational and tertiary degree to high risk industries, these educational technologies are engaging, they place students at the centre of the learning process, force students to be active and serve as a great risk-free enviroment for safety critical training. Despite being extensively used for trianing operators in aviation, maritime and rail, there is still a perception that high fidelity simulators are always a preferred technology. The assumtion is the similar to a real enviroment, the better. However, recent research has shown that simulators and training devices will never fully reproduce reality and the low fidelity ones are as helpful if employed with appropriate pedagogy and support materials. In this research project, the objective is to assess how different simulator and training device tehnologies can employed to enhance training. Which tasks and skills can be developed accross a range of devices taking into consideration learning objectives, training outcome, quality, length and costs?

Governments have a key role to play in achieving sustainable development and addressing climate change. The object of this research is to synthesise policies, plans and strategies that will assist with this transition. While commitments have been made at the international level, and some organisations have made improvements at the local level, there is a strategic gap between the two that has not been fully researched. PhDs can be on either sustainability or climate change and take a theoretical or applied approach. The methods used include case studies, comparative analysis, policy analysis, stakeholder interviews, or surveys.

In networks with distant parties, light provides an excellent way to transmit quantum information because of its fast propagation and low decoherence. However, these advantages are accompanied by a drawback - the lack of appreciable interactions between photons at the single-photon level, which makes it more challenging to create entangled multi-qubit states with photons compared to other carriers of quantum information. The objective of this theoretical project is to develop optimal state preparation procedures and incorporate them into strategies for showcasing novel quantum cybersecurity protocols.

Flight decks are constantly evolving. New technology is constantly implemented in it to enhance safety. However, with such new additions there are also some challenges that might arise. These need to be understood to maintain safety. Touch screen technology is one of the newest additions in the flight deck. In this project, the interaction with such technology during various flying conditions will be explored. Any issues pilots might face when interacting with such technology will be understood. Training required to know when to use and when not to use touchscreen technologies will be examined too. As seen with many other types of flight deck technologies; a new piece of technology that starts off in the commercial airline industry might trickle down into the general aviation industry also. Hence, suitable recommendations will be made for the wider aviation industry.

Graph machine learning, graph neural networks, in particular, is the frontier of deep learning. There has been an exponential growth of research on graph neural networks (GNNs) in the last few years, mainly focusing on how to develop accurate GNN models. The trustworthiness of GNNs is less considered. In this project, we will explore how to develop trustworthy GNN models. The key aspects, including robustness, explainability, fairness, and privacy, will be taken into consideration when developing GNN models.

This project aims to develop nano-catalysts with high catalytic activity and rapid gas detachment properties for efficient fuel gas production. Heterogeneous electrocatalytic gas evolution reactions are important for clean energy generation and storage technologies, but high overpotentials caused by slow gaseous products’ detachment from catalyst surface severely hinder their efficiencies. Expected outcomes include insights into gas bubble formation and evolution during electrocatalysis, effective catalyst structures to mitigate negative effects of gas bubble formation, and improved catalytic efficiency of gas evolution reactions and develop high performance electrocatalysts for fuel gas production.

Photons are low-noise and flexible quantum systems, perfect for quantum communication and quantum information processing. However, to date, it has not been possible to create key photonic quantum states such as high-fidelity states of many correlated photons and complex heralded entangled photon states. These projects will use high-efficiency photon-pair sources developed at Griffith University and world-leading superconducting photon detectors to develop and generate these important photonic quantum states. 

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their security. One particularly insidious threat is the emergence of adversarial attacks, wherein malicious actors deliberately manipulate input data to deceive deployed DNNs, leading to misclassifications or compromised performance. These attacks pose a significant challenge, demanding a comprehensive understanding of their mechanisms and the development of robust defences. This proposal aims to delve into the intricacies of adversarial attacks, exploring their impact on DNNs and proposing effective strategies to fortify these models against such sophisticated threats.

Advancements in analytical capabilities make it possible to simultaneously measure a comprehensive suite of physiologically important biomolecules in living organisms. These molecules can provide a ‘snapshot’ of the health and general well-being of an organism. This research project aims to establish robust methodologies to make molecular monitoring a reality. The PhD project will apply untargeted metabolomics and lipidomics analysis to evaluate and compare the status of aquatic species from pristine and human-impacted locations, with the goal of establishing biomolecular signatures as indicators of environmental health.

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their security. One particularly insidious threat is the emergence of backdoor attacks, which involve stealthily implanting malicious features or patterns during model training, enabling unauthorised access or manipulation of the neural network's behaviour under specific conditions, compromising its integrity and functionality. Notably, recent reports have raised suspicions about a significant number of pre-trained DNN models from Model Zoo being vulnerable to backdoor attacks. This project investigates backdoor attacks on DNNs, aiming to expose attack mechanisms, assess real-world implications, and propose detection/mitigation strategies for robust AI systems. The exploration includes defining and elucidating backdoor attacks, examining implantation techniques, showcasing instances, and consequences, and analysing recent cases for lessons.

This project aims to research into the application of AI to assist with learning and teaching (L&T). There are many aspects of L&T that can benefit from the use of AI. However, instead of having AI playing a central role, e.g. run a class, this research focuses on the use of AI in a supporting role such as providing feedback to students, assist teachers in marking students work, or even directly mark student’s formative work etc. This applied research designs and develops AI tools to assist in specific L&T activities, applies these tools and evaluate the results. It explores the scenarios where AI tools are of benefit and determines how the tools should best be utilised.

This project will explore hosting three of the most ubiquitous chemical developers for fingerprints within cavitands so as to modify their solubility. The aim will be to make those developers water-soluble, thereby allowing the elimination of costly and environmentally damaging solvents from common forensic treatments.

Globally, the whale watching industry has been increasing in size and economic value since the 1990s, yet, little is known about the importance of this sector to the local economy. This research project aims to update and establish the latest figures on this sector for ɫ����վ recognising the increase of whale watching and swim with whales in ɫ����վ. The most recent estimates on the contribution of whale watching to the economy date back to 2008, where it was found that over 1.6 million people went whale watching, generating AUD $47 million in ticket expenditure and AUD $264 million in total tourism expenditure. This project will involve the analysis of historic customer number and revenue data collected by whale watch operators and may also involve collecting data directly from whale watch participants via an expenditure survey.

The Daley Family

Top up scholarships supporting candidates to undertake a PhD in medical research, awarded in honour of Mr Thomas Daley

Despite the recent advancements in Generative AI and Large Language Models in particular, these models still struggle to provide fair outcomes across societal groups. At the same time, models need to continually learn to adapt to our ever-changing world. This project looks at developing models that can mitigate bias in the face of dynamic updates to its pre-trained and fine-tuned knowledge base.

Prompt identification and classification of algae and cyanobacteria species are paramount for water utilities and city councils to minimize negative ecosystem and public health effects. While real-time sensors can provide an estimation of total biovolume, traditional light microscopy is still the most common method for manually assessing morphological features and, in turn, species. This project will build on existing algorithms and apply deep learning to automatically identify various cyanobacteria species and their cell counts from microscopy images. A successful outcome would be extremely beneficial for several organizations, leading to more proactive management of toxic cyanobacteria blooms.

This project will develop a data-driven approach to predict shoreline dynamics based on remotely sensed shoreline data using machine learning algorithms. The ability to forecast coastal shoreline dynamics would facilitate improved coastal management and planning to mitigate both long-term and short-term erosion events through, for example, scheduling and designing of nourishment activities. The Gold Coast beaches provide the perfect natural laboratory due to a wealth of shoreline and oceanographic monitoring data. The shoreline data includes in situ beach profiling and remotely sensed camera and satellite derived shoreline position data. The extensive data available presents a great opportunity to develop the proposed data-driven approach to predict the shoreline dynamics.

Multiple projects available and can be tailored to applied mathematics, computer science, or physics focus. Central ideas are around construction of simulation frameworks for a variety of high impact plasma and electron transport applications, such as atmospheric lightning discharges on Earth or other planets, low temperature plasma manufacturing processes, astrophysical plasmas, or fusion plasmas. Areas of investigation can be tailored to candidate expertise & interests, including numerical solution techniques for transport equations, the closure problem, machine learning and AI in computational science, kinetic or Monte Carlo methods, or inclusion of accurate atomic and molecular physics data into plasma models.

A variety of projects are available in different theory and modelling areas to describe a variety of important physics scenarios important to fusion plasmas, such as those anticipated in tokamaks. Equilibrium plasma discharge, tokamak disruption, runaway electrons, edge-plasma, and surface wall interaction applications are examples of focus applications that are available for study.

The current wave of deep learning and AI research has yielded many advances in how tools such as neural networks, optimization, or uncertainty quantification can be used to improve modelling capability for a number of useful applications. Projects are available in the development of robust and transparent machine learning and AI techniques that can be employed to augment existing computational modelling techniques (e.g. surrogate models, reduced order models, etc) or to provide new avenues of solution (e.g. PINNs as a famous example).

Projects are available in different theory and modelling areas to describe and simulate electron dynamics in liquid forms of rare gases, such as argon, xenon or krypton. This research helps inform the operation, design, and interpretation of large scale dark matter or particle physics detectors that use volumes of liquid as a scintillation background. Understanding how electrons move through these liquids, or even across gas-liquid interfaces, is crucial to identify the detection signals produced by the rare exotic species interacting with the liquid background.

In transition to new green energy sources, one thing often ignored is the electricity network infrastructure itself. Most networks rely on a gas insulator sulphur hexafluoride (SF6) - which is about 23,000 times worse than CO2 as a greenhouse gas. Projects are available in different theory and modelling areas to describe and simulate the way discharge arc current forms (and extinguishes) in dielectric gas insulators contained in high voltage hardware. The application of this research is help better understand how to replace SF6, which is the current standard insulating gas used in practically all electrical network hardware.

This project aims to identify and characterise a previously uninvestigated meta-stable state of Ytterbium with a calculated lifetime of 1 second, making it an idea candidate as a quantum memory as well as holding interest for precision tests of fundemental physics as well as a metrological reference.

A previous experiment at the ANSTO Lucas Heights nuclear reactor investigated the possibility of neutrino flux altering the flow of time through weak force symmetry violation. This project would consist of analysing that data as well as modelling other situations (low energy solar neutrinos) where a time variation might be induced.

Two qubit quantum gates are an essential building block to realising quantum computers. In trapped ion quantum computers these are presently limited by the vibrational confinement of the atoms, however there are proposed protocols which use strong optical forces to surpass this speed limit by using high intensity pulsed lasers. This project would investigate the spectroscopic limits of this approach for several common ions used in trapped ion quantum computing, with the goal of being able to bound the potential maximum fidelity of this approach.

Optical Metasurfaces, that is 2D arrays of sub-wavelength nanoresonators, have revolutionised the field of photonics by enabling compact, flat and versatile optical devices. The vast majority of optical metasurfaces are based on solid state materials like silicon or TiO2 which cannot be modified after fabrication. This project will explore the application of photoresponsive polymers to modulate the behaviour of optical metasurfaces. Nanoresonators are highly sensitive to surrounding refractive index, so small changes in the polymer property can have a significant effect on the light-matter interaction. In this project, optical metasurface will be coated with photoresponsive polymers, and spatial control over the refractive index will be explored using patterned optical stimulus. The final goal is to develop dynamic photonic devices driven by light.

Aviation may be economically and socially sustainable but faces real environmental challenges. Globally, emissions from air travel need to be addressed in line with climate change advice from the Intergovernmental Panel on Climate Change (IPCC). There is a need to decarbonise the aviation industry and to move towards Net Zero Emissions (NZE), particularly when demand for international and domestic travel is gaining momentum. The PhD research could focus broadly on the relationship between aviation and climate change, whether mitigation or adaptation. It could alternatively focus on the characteristics of future sustainable air travel, the sustainability strategy of a particular airline or airport, the impact of new technologies (e.g. UAVS, eVTOL) on environmentally sustainable air travel, or the development alternative fuels such as SAF (Sustainable Aviation Fuel), hydrogen or electricity.

A growing population and urbanisation have led to significant increases in the built environment needs and associated waste generation. There are calls for government and industry to develop better methods in managing waste across the built environment sector, including that from construction and demolition. There are the following strategic priorities: 1) Reducing the impact of waste on the environment, 2) Transitioning to a circular economy for waste, and 3) Building economic opportunity. The PhD research could focus broadly on the relationship between waste and the built environment, or be more specific on a particular built environment sector (e.g. buildings or transport) or waste type (e.g. from Construction & Demolition). It could be quantitative or qualitative in approach. The research could align closely with opportunities associated with the local Queensland Built Environment Waste Strategy and Action Plan, as well as construction around the Brisbane 2032 Olympic and Paralympic Games.

After absorption of a photon by a molecule an electron is elevated into an electronically excited state and follows the most favourable energetic pathway to either relax back to ground state (fluorescence) or undergo an intersystem crossing into a triplet state where it can initiate a chemical reaction or relax back to the ground state (phosphorescence). What if, instead of leaving these alternate pathways to chance, we could control the specific pathway the electron follows, by enhancing the electric field intensity at specific wavelengths or favour electron intersystem crossing processes with strong magnetic fields. This project will explore the engineering of electric and magnetic fields with dielectric nanoparticles and then bring organic molecules into close proximity and observe the resulting electron dynamics.

We are seeking a dedicated PhD candidate to contribute to an innovative research project that applies cybersecurity principles to hyperspectral satellite imagery and machine learning. The project focuses on developing secure, data-driven algorithms for safeguarding sensitive environmental data and ensuring the integrity of the monitoring systems. The role will involve designing advanced techniques to process satellite data securely, addressing potential cyber risks in remote sensing applications, and collaborating with industry partners to ensure robust and practical climate change mitigation strategies. Weekly progress reporting and a strong focus on the intersection of cybersecurity and data science make this an ideal opportunity for someone looking to apply their cybersecurity expertise to critical global challenges.

While global attention has rightly focused on the loss of threatened species, a critical gap exists in quantifying the decline of common species. Common species, often overlooked in conservation priorities, play foundational roles in ecosystems, ensuring the stability of food webs, nutrient cycling, and ecosystem services such as pollination and climate regulation. Yet, evidence shows that many of these once-abundant species are experiencing significant population declines due to habitat loss, climate change, pollution, and human activity. This project aims to investigate which species have declined, where this decline has occurred, and potential drivers of decline.

As the call to end native forest logging grows louder due to its detrimental impacts on biodiversity, carbon storage, and ecosystems, the transition to plantation-based timber production becomes a critical pathway. However, this shift demands more research into where we should expand plantations and how to manage them sustainably. Current knowledge gaps include identifying optimal locations for plantation expansion that minimize biodiversity loss and land-use conflicts while maximizing carbon sequestration. Equally important is developing sustainable management practices that avoid the pitfalls of monocultures, promote biodiversity within plantations, and maintain soil health and water quality.

The New South Wales Government’s commitment to creating the Great Koala National Park represents a landmark opportunity to reverse the rapid decline of koalas and protect their habitats. However, critical knowledge gaps remain regarding the spatial extent, specific locations, and conservation actions needed to halt koala extinction and put them on a path to recovery. This research project aims to provide a spatial plan to ensure the park’s success in conserving koalas. By mapping critical koala habitats and assessing threats such as deforestation, disease, and climate change, we will identify priority areas for protection, management and restoration. Additionally, we will evaluate the specific actions needed, such as habitat corridors, predator control, and disease management, to bolster koala populations. Beyond direct koala conservation, the Great Koala National Park offers substantial co-benefits that warrant quantification. These include enhanced biodiversity conservation by protecting habitats for other threatened species, increased carbon sequestration through forest preservation and restoration, and economic benefits from eco-tourism.

ɫ����վ’s commitment to protecting 30% of its land and sea areas by 2030, in line with global biodiversity targets, represents a crucial step toward halting biodiversity loss and restoring ecosystems. However, the roadmap to achieving this ambitious goal remains unclear. Critical questions about the location of new protected areas, the integration of Indigenous land management practices, and the economic and social implications of expanding protected areas need to be addressed through robust research. This research project aims to investigate the pathways for ɫ����վ to achieve the 30% by 2030 target by identifying priority areas for protection. A key focus will be on balancing biodiversity conservation with other land uses, such as agriculture, forestry, and urban development, to ensure the transition is equitable and sustainable.

Climate change is rapidly altering ecosystems, pushing many species to the brink of extinction as their habitats shrink or disappear entirely. However, a critical gap in our understanding remains: which species are most at risk of having no viable habitats in the future due to rising temperatures, shifting rainfall patterns, and other climate-related changes? This research aims to address this urgent question by identifying species likely to face complete habitat loss by the end of the century. Using advanced climate modeling, species distribution data, and habitat suitability analysis, we will predict which species will be unable to find suitable environments as climate change intensifies. This research will focus on species with narrow habitat ranges, specialized ecological needs, and limited dispersal abilities, which make them particularly vulnerable.

Auxetic structures are gaining attention for their adjustable properties in varying conditions, serving as a core in sandwich structures to mitigate energy transfer during impact, shock loading, crushing, and bending. These structures, conducive to personal protection equipment and protective constructs, are being developed using modern additive manufacturing. The project aims to analyse the acoustic and vibration response of an auxetic core in a multilayer structure through finite element method. Numerical modelling of 3D printed parts will involve establishing a constitutive relationship and considering material properties to investigate the eigenvalue problem.

Auxetic structures are gaining attention for their adjustable properties in varying conditions, serving as a core in sandwich structures to mitigate energy transfer during impact, shock loading, crushing, and bending. These structures, conducive to personal protection equipment and protective constructs, are being developed using modern additive manufacturing. The project aims to create efficient sandwich composite with an auxetic core through novel designs, optimizing their performance using finite element tools. Numerical modelling of 3D printed parts will involve establishing a constitutive relationship, considering material properties at different strain rates and temperatures to enhance the efficiency of the material model. This approach enhances load-bearing capabilities and provides a predictive tool for future researchers analysing the material response of additively manufactured components.

This project focuses on the integration of Generative AI into cyber resilience strategies to enhance an organization's ability to anticipate, withstand, and recover from cyber threats.

Spinal cord injury is a severe personal injury. PRECISE's BioSpine team have developed non-invasive therapeutic technology to faciliate recovery of sensorimotor function after spinal cord injury. As part of this larger project, we aim to gain better understanding of how the human central pattern generators might be modulated through BioSpine therapy.

Spinal cord injury is a severe personal injury. PRECISE's BioSpine team have developed non-invasive therapeutic technology to faciliate recovery of sensorimotor function after spinal cord injury. As part of this larger project, we aim to create a technological framework to enable control of the rehabiliation such that it promotes healthy bone adaptation and lowers risk of bone injury.

Hip osteoarthritis is a major cause of disability in ɫ����վ, with growing prevalence in our aging population. Controlling tissue loading is a promising non-invasive method to manage hip osteoarthritis. PRECISE researchers have developed wearable technology to enable monitoring and control of hip tissue loading during daily activities outside the laboratory (e.g. at home). However, this technology requires development and testing of user interface software to ensure the technology is effective in use and acceptable by users.

Musculoskeletal conditions are the single largest cause of disability globally. New research technologies can monitor and control musculoskeletal tissues to prevent their injury and promote healing in a laboratory setting. However, to be effective, these technologies must be translated into clinics and homes. PRECISE researchers are developing a computer vision based technology that will function in allied health clinics to provide insights into musculoskeletal tissue loading in a clinical setting.

Female participation in ɫ����վn Football is rising at a rapid rate. So too are the rates of severe musculoskeletal injuries in this population. The causes of these injuries remains contentious in the research community. PRECISE researchers have developed a set of predictive tools that can estimate risk of tissue injuries with high levels of certainty. What remains to be established is what interventions can be performed to mitigate these risks based on mechanistic understanding of tissue injury.

Recent advances in dynamic radiography and computational modelling has radically challenged conventional understanding of foot shape and function. PRECISE researchers are pushing the boundaries of knowledge regarding the evolution of the human foot and its contributions to human performance and dysfunction.

Personalised digital twins of a patient's neuromusculoskeletal system enables accurate in silico planning, simulation, and surgical translation for a variety of conditions from paediatric anterior cruciate ligament reconstruction to proximal femoral osteotomy to adult reconstruction of the scapholunate interosseus ligament. PRECISE researchers have developed platform technology to conduct research in this field.

Precision modelling for health and peformance holds great promise. However, bringing these research technologies to the real-world to create impact requires development of methods that enable an "escape from the laboratory". The combination of wearable devices with physics-informed artificial intelligence is emerging as a promising approach to achieve field-friendly technology that retains accuracy and reliability.

This innovative research PhD project aims to uncover essential insights into the genetic foundations of coffee tree productivity and flavour profiles. By developing targeted genetic markers for both productivity and flavour-related genes, we will identify key quantitative trait loci (QTLs) linked to traits such as yield and flavour. The project aims to discover new polymorphisms influencing coffee crop productivity and flavour. Additionally, the project will explore correlations between key genomic regions associated with quantitative traits and environmental factors. Using advanced association mapping and statistical models, we aim to clarify the relationships between different coffee varieties and their flavour attributes.

Coffee flavours embody the complex sensations we experience when sipping coffee, encompassing aromatic notes, tastes, and bodies. Recognising these components is essential for appreciating ‘Speciality Coffee,’ where each cup aims to showcase unique flavours like fruity, nutty, floral, or spicy. By exploring these flavours through the proposed PhD project, within the context of ɫ����վn environments, we aim to connect flavours to our sensory memories using the flavour-and-character wheel. While identifying specific flavours can be challenging, employing flavour categories facilitates this process. The PhD candidate will be equipped with state-of-the-art learning in sensory science and analytical chemistry to comprehend the science behind coffee flavour. Further engagement with key stakeholders will determine market perspectives, with a focus on consumer-driven research.

Shaping the future of transport for Brisbane 2032

This project harnesses quantum computing to shape Brisbane’s future, enhancing transport and logistics for the 2032 Olympics and beyond. Our PhD program offers peer support, industry connections, and access to data and models. Recognized for research excellence, our graduates secure roles in top universities, transport agencies, and consultancies. Your research will develop quantum solutions to improve efficiency, safety, and sustainability in event logistics. With industry collaborations dating back to 2025, this program provides a strong research foundation. Ideal candidates have expertise in logistics, transport planning, or operations research, with skills in Python, R, GIS, AI, or big data. A top-up scholarship of A$11,000 per year for three years is available.

This project aims at establishing new zebrafish models of motoneuron degeneration or neurodegeneration per se. We will use state-of-the-art genome editing tools (optimised CRISPR/Cas9 approach) to manipulate selected genes of interest to both validate their predicted pathogenicity and generate animals developing neurodegeneration. These models will further be used to investigate the underlying degenerative mechanisms and establish drug screening/discovery programs.

Neurexins are a family of genes that have been associated with several neurological diseases. We have generated a series of innovative zebrafish CRISPR-mutants that should allow to better understand the role of these genes in the developing brain. We will combine single-cell transcriptomics studies, high-end imaging, and behavioural approaches to highlight their critical function in brain development and plasticity.

Whilst many current drugs are derived from nature, many more bioactive molecules have still to be discovered. To help speed up discovery, we will develop i) a unique multipurpose zebrafish model combining different transgenic fluorescent markers/sensors and ii) automated assays to screen existing diverse chemical libraries for bioactive molecules. Validated assays will then be used to screen natural product libraries and start looking for the drugs of tomorrow.

This project aims at establishing new zebrafish models of epilepsy. We will use state-of-the-art genome editing tools (optimised CRISPR/Cas9 approach) to manipulate selected genes of interest to both validate their predicted pathogenicity and generate animals developing seizure and/or Developmental and epileptic encephalopathy . These models will further be used to investigate the underlying pathogenic mechanisms, establish drug screening/discovery programs and translate our work in association with clinicians.

The increase in bacteria acquiring resistance to current antibiotics, and a reduction in development of new antibiotics by the pharmaceutical industry over the past years, is placing a significant burden on global health care, with the World Health Organization noting that antibiotic-resistant pathogens represent an imminent global health disaster for the 21st century. Our research is focussed on investigating alternative therapeutic strategies to break antibiotic resistance.  Metal-ion homeostasis is critical for bacterial survival, and elevated metal ion levels can be toxic to bacterial pathogens.  Ionophores are chemical compounds that facilitate transport of metal ions across biological membranes. Together with our collaborators, we have identified ionophores that are able to break antibiotic resistance by destabilizing bacterial metal homeostasis. This project will extend our work in this area, through development and evaluation of new ionophores.

The hand, foot and mouth disease causing agent enterovirus 71 engages a variety of receptors on the surface of host-cells prior to entry. These receptors include the P-selectin glycoprotein ligand-1 (PSGL-1), the scavenger receptor class B member 2 (SCARB2), glycosaminoglycans (GAG) and sialylated glycans. The interplay between these receptors is still poorly understood. The types of GAGs and sialylated glycans the virus binds to have not been fully investigated, and we believe that given our progress with GAG-like binding inhibitors they may be more important than previously reported. Furthermore, in our experience different cell-types have different susceptibilities to glycan-based binding inhibitors, suggesting that cell binding events may be more complicated than previously characterised.  This multidisciplinary research project involves the differentiation of various cell types and subsequent functional assays to investigate virus–cell binding events, glycan-array experiments, cell-based chemical combination assays using glycans, competition STD-NMR experiments and crystallography using purified virus particles. 

Exosomes are vesicles that are secreted from cells and appear to have roles in the tumour microenvironment, including in metastasis. These vesicles are therefore thought to be invaluable in both a diagnosis setting as well as therapeutic targets. Little is known about the cell surface changes in glycans and glycan-recognising proteins on exosomes. This project will explore these changes using a multidisciplinary approach that may identify potential biomarkers and therapeutic targets that could be used in diagnosis and drug discovery, respectively. 

Human parainfluenza viruses (hPIV) cause serious respiratory infections, especially in infants, the elderly, and immunocompromised individuals. No vaccines or specific antiviral treatments currently exist. This project focuses on the hPIV surface glycoprotein haemagglutinin-neuraminidase (HN), which plays a key role in virus entry and spread by interacting with host cell sialic acid. The project aims to deepen our understanding of HN function and develop potent inhibitors that block HN–sialic acid interactions. With X-ray crystal structures of HN available, structure-guided drug design, molecular modelling, fragment screening, and synthetic chemistry will be used to identify new antiviral compounds. These will be tested using biochemistry and structural biology techniques on both the whole virus and recombinant HN protein. A student on this project can focus on one area—such as molecular modelling or protein biochemistry—or gain broad experience across multiple approaches to antiviral development.

Human metapneumovirus (HMPV) is a major cause of lower respiratory disease in infants, second to RSV, with no approved antiviral drugs or vaccines available. High-risk groups include young children, the elderly, and immunocompromised individuals. HMPV has three surface glycoproteins—F, G, and SH— used for infection, with the F protein playing a central role in host cell binding and membrane fusion. The F protein interacts with host receptors such as DC-SIGN, L-SIGN, and heparan sulfate (HS), but the structural basis of these glycan interactions is not fully understood. This project aims to investigate how HMPV engages host glycans and to develop inhibitors that block these interactions. The research will use biophysical and cell biology methods to define the glycointeractome of HMPV and identify potential antiviral targets. A student on this project may focus on a specific area or engage in several aspects of the research, from structural analysis to inhibitor screening.

The upper airways are a major entry point for many pathogens, including influenza viruses, Streptococus pyogenes (Strep A), and coronaviruses. To combat pathogens that infect upper airways, we aim to develop new lipid, protein, and messenger RNA (mRNA) technologies. We have developed a novel lipid delivery system (LDS) that delivers mRNA or proteins efficiently intranasally and is capable of storing mRNA and biological products at room temperature for extended periods of time. By incorporating lipid-linked sugars (glycolipids), secretory immunoglobulin A (IgA)-mediated mucosal immunity is enhanced, which reduces infectivity. This foundation will be built upon for the delivery of vaccines, antibodies and antiviral proteins to the mucosa, providing enhanced protection against influenza A and B viruses as well as Strep A infections. It involves forming delivery systems, testing them in pre-clinical models, and performing immunological and functional tests.

An ideal membrane for guided tissue regeneration (GTR) should be resorbable and shapable with particular mechanical strength for space maintenance, which can’t be achieved using the current non-resorbable (Ti mesh) and resorbable membrane (collagen, which lacks mechanical strength); in addition, the present membranes lack the capacity of infection and inflammation control and unable to create a favorable environment for osteointegration. Moreover, the coordination between neurogenesis, angiogenesis and osteogenesis suggests the importance of regenerating an innervated and vascularized functional bone tissue. At the same time, this point has been ignored in the current GTR membrane design. The project will fill these gaps by answering the research question: How to make a resorbable, anti-infectious, anti-inflammatory GTR membrane with sufficient mechanical strength for functional bone regeneration.

In aging individuals, multiple organs and tissues are under a long-term, chronic and sustainable state of inflammation, also known as inflammaging. Recent evidence suggests that macrophages are the central component in initiating inflammation. Macrophages are a class of innate immune cells that originated from the mononuclear phagocyte system (MPS). The project is to understand the potential mechanisms of aging macrophages in regulating the healing environment for mesenchymal cell differentiation, and to develop nanomaterials to regulate the aging process of macrophages and cell polarisation, leading to tissue regeneration.

A hematoma that forms immediately following tissue damage, such as a fracture, is a natural tissue healing scaffold and plays a critical role in tissue repair and regeneration, like in fracture healing and subsequent bone regeneration. The project aims to understand the characteristics of hematoma and manipulate its properties to accelerate the tissue healing process.

The 1-azaadamantane core synthesised via a double aza-Prins reaction resulted in compounds with remarkable equal potency against replicating and non-replicating Mycobacterium tuberculosis. THis project will explore development of analogues to explore SAR

We have made the exciting discovery that the clinically used antimalarial drug proguanil has much more potent activity than previously thought. The activity of proguanil has, up until now, been thought to be due to its in vivo cyclization metabolite cycloguanil, a DHFR inhibitor, and by potentiating atovaquone activity. In this project, cyclization blocked analogues of proguanil will be investigated as potential new combination partners for atovaquone. Approaches will include in vitro growth inhibition assays, combination studies, time of kill assay and in vivo efficacy testing in murine models of malaria.

Nontypeable Haemophilus influenzae (NTHi) causes middle ear infections in children, sinusitis in adults, acute bronchitis, and exacerbations of chronic obstructive lung disease. Neisseria gonorrhoeae (Ng) infects human mucosal surfaces, leading to the sexually transmitted infection gonorrhea. Both of these bacteria are increasingly resistant to antimicrobial treatments, posing significant challenges to healthcare and endangering our ability to treat these diseases. Currently, there are no vaccines available for these pathogens. They have evolved mechanisms to evade the human immune system, making it difficult to develop effective vaccines. Our laboratory has discovered a shared characteristic between these two bacteria: the aberrant expression of a unique sugar. The project aims to use different approaches to design and synthesize carbohydrate-based vaccine antigens, which will be tested in mice. Successful completion of the proposal will enable the development of a potential vaccine that may provide a new solution for the prevention of NTHi and Ng infections.

Antimicrobial resistance is a growing concern in pathogenic Neisseria species such as Neisseria gonorrhoeae and Neisseria meningitidis, which are responsible for causing sexually transmitted infections and meningitis, respectively. These bacteria have developed resistance to multiple antibiotics, including penicillin, tetracycline, and fluoroquinolones, which were previously used to treat these infections. The emergence and spread of antimicrobial resistance in pathogenic Neisseria is primarily driven by the acquisition of resistance genes through horizontal gene transfer. This has led to the development of multidrug-resistant strains that are becoming increasingly difficult to treat, posing a significant threat to public health. To address this issue, there is a need for the development of new antibiotics and alternative therapies that are effective against multidrug-resistant strains. The goal of this project is to develop a novel treatment for pathogenic Neisseria.

Neisseria gonorrhoeae is a bacterial pathogen that causes the sexually transmitted disease gonorrhea by infecting male urethral and female cervical tissues. One of the major virulence factors of N. gonorrhoeae is Lipooligosaccharides (LOS), which can take on multiple glycoforms due to the phase variation of the genes involved in LOS biosynthesis. The structure of gonococcal LOS is capped with N-acetyl-5-neuraminic acid (Neu5Ac), but the bacterium cannot synthesize the CMP-Neu5Ac required for LOS biosynthesis and must acquire it from the host. While the core-oligosaccharide of LOS is assembled in the cytoplasm, our published study revealed that the alpha-2,3-sialyltransferase Lst, previously thought to be a surface-exposed outer membrane protein, is located inside the cell. This suggests the existence of a transport system or trans-sialidase to transport CMP-Neu5Ac from the host to inside the cell. The goal of this project is to identify a novel CMP Neu5Ac transporter in Neisseria.

Over 65 years ago, Guinea pig serum was fortuitously discovered as an effective anti-tumour agent against transplantable lymphomas. Subsequent investigations revealed that L-asparaginase in the serum was responsible for this remarkable anti-tumour property. The anti-tumour properties of L-asparaginases are primarily due to the depletion of exogenous L-asparagine, which is crucial for the growth of tumour cells, as they are essentially auxotrophic for L-asparagine. Despite its discovery, the identification of guinea pig serum L-asparaginase has been problematic, as it has only been found in guinea pig serum and not in the sera of other species studied, including mice and rats. This project aims to determine the genetic and biochemical origins of liver and serum enzymes in guinea pigs and related species, which also have both isozymes.

Cancer therapies have experienced a tremendous revolution with the introduction of therapies that use monoclonal antibodies that specifically target cancer cell surface targets and immune-checkpoint receptors. More than 95% of the protein receptors targeted by these immunotherapy agents are in fact glycoproteins, but to date the impact of receptor glycosylation in precision medicine is still not understood. In this close collaboration with colleagues from the Peter MacCallum Cancer Centre students will be introduced to biochemistry and immunology laboratory workflows that include (but not limited to) SDS-PAGE, Western blotting, Proteomics and Glycomics sample preparation, acquisition and data analyses and gain general knowledge in Biochemistry and Glycobiology. Cancers that are being targeted in this project include Leukaemia, prostate cancer, melanoma, Head and Neck Cancer, Hepatocellular carcinoma or Colon cancer are investigated.

Receptor glycoproteins are highly important signalling molecules in controlling cell communication and interaction. Dysregulation of these signalling pathways is frequently associated with diseases such as cancer and chronic inflammatory conditions. However, the role their glycosylation plays for protein structure and interaction is still poorly understood. Type III family of receptor tyrosine kinases such as c-KIT (also known as Stem Cell Factor receptor or CD117), PDGF-receptor-α and β, CSF-1 receptor and the FLT3 receptor play a vital role in the pathogenesis across different types of cancer. As part of a larger, collaborative project (Mater Research, Brisbane and ɫ����վn Red Cross Lifeblood) a variety of student projects are available that include aspects of mass spectrometry applications (proteomics, glycomics and glycoproteomics) next to structure biology, molecular dynamics simulation, cell culture, Western Blot, electrophoresis and other standard biochemistry techniques.

Understanding how cancer alters protein glycosylation offers untapped potential for precision medicine. Glycosylation—where sugars (glycans) are added to proteins—is highly individual and influences key biological processes, including cancer progression. Unlike genomics, only glycomics and glycoproteomics can decode the dynamic “glycocode” of cancer. At the ɫ����վn Cancer Research Foundation (ACRF) International Centre for Cancer Glycomics, we collaborate with clinical partners to map cancer-specific glycomes using advanced tools like Laser Capture Microdissection and glycan sequencing. Our research focuses on cancers such as leukemia, prostate, melanoma, ovarian, head & neck, and colon cancer. We aim to discover novel biomarkers and therapeutic targets. Student projects are available and involve hands-on lab work in SDS-PAGE, Western blotting, proteomics, glycomics, and data analysis. This is a unique opportunity to gain experience in a cutting-edge, interdisciplinary environment at the interface of clinical and molecular cancer research. Be part of the cancer glyco-revolution.

Our recently published study in Nature Communications identified the musculoskeletal protein FHL1 as a critical host factor for chikungunya virus (CHIKV) replication, but not for Ross River virus. We showed that during acute infection, FHL1 interacts with the viral protein nsP3, boosting viral replication and driving disease severity. Notably, FHL1 knockout mice showed significantly reduced disease, confirming its central role. As a next step, we aim to determine whether FHL1 also contributes to the chronic and persistent phases of alphavirus disease, particularly for CHIKV and Onyong-nyong virus. Using transgenic mice expressing FHL1, this project will explore its role in viral persistence and long-term pathology, extending our previous findings to better understand post-acute disease mechanisms.

We are pursuing a new project exploring the role of previously uncharacterised host factors—H2-D1 and Lemd3—in alphavirus infection. While these factors have emerged in early discussions, no published studies currently define their contribution to viral entry, replication, or disease progression. Our goal is to determine their function, particularly in arthritogenic alphaviruses, with additional investigations into encephalitic alphaviruses in collaboration with US-based colleagues. To achieve this, we will generate CRISPR-Cas9 knockout mice in partnership with WEHI, and derive knockout cell lines for mechanistic studies of virus-host interactions. By comparing responses across multiple alphaviruses, we hope to identify virus-specific vs shared host dependency. Where possible, we will also validate findings in human clinical samples. This is a first-in-field investigation with strong potential for high-impact publications.

Building on our recent development of a novel split trans-amplifying mRNA vaccine platform, this project explores its application to several high-priority pathogens, including Japanese encephalitis virus, Nipah virus, and Rift Valley fever virus—viruses that represent major global health concerns and for which improved vaccines are urgently needed. Each project will focus on a specific virus and involve in vitro characterisation of mRNA vaccine constructs, followed by in vivo studies to assess immunogenicity and protective efficacy in relevant animal models. With its flexibility, safety, and capacity to deliver multiple antigens, this mRNA platform offers strong potential for future vaccine development and could contribute meaningfully to the field of infectious disease prevention.

This project investigates how viral genetic factors contribute to differences in disease severity during chikungunya virus infection. Using clinical isolates from patients with mild and severe disease, we have identified genetic differences between the strains and generated infectious clones to study their behaviour. In our established mouse model, these clones faithfully reproduce clinical phenotypes, confirming that viral genetics influence disease outcome. The project will involve comparative genomic analysis, infection studies, and host transcriptomic profiling to uncover viral determinants that drive severe versus mild disease. This work aims to deepen our understanding of CHIKV pathogenesis.

This project aims to develop native mass spectrometry methods for characterising biomolecules that underpin emerging disease therapeutics. Native mass spectrometry is a rapidly growing biophysical technique used in drug discovery research – this project is one of few opportunities in ɫ����վ to develop skills with this emerging and continually developing methodology. Potential biomolecular targets to be investigated include soluble and membrane proteins and structured RNAs, and their complexes with other proteins, nucleic acids and/or lipid binding partners. Development of these methods will facilitate the fundamental understanding of these molecules and further drug discovery by allowing fragment, or other, screening campaigns to discover novel binding compounds, or characterise previously identified therapeutic binding compounds. This can be applied to various diseases areas including cancer and infectious diseases

This project aims to develop new nucleic acid chemistries to facilitate functionalisation and improve the biological stability of oligonucleotide therapeutics (mRNA). These new functionalisation chemistries will be designed to allow fast conjugation and screening of groups that improve cell targeting, cell uptake, and metabolism of the oligonucleotide therapeutics. The project will involve the design and synthesis of nucleotide phosphoramidite precursors monomers, the semi-automated synthesis of oligonucleotide sequences, and performing oligonucleotide bioconjugation and functionalisation assays. This project has a strong focus developing industry-ready candidates, creating valuable IP, and impactful publications.

Heart failure is a major global pandemic affecting more than 38 million people worldwide. It has been suggested that poor oral hygiene and periodontal diseases are related to a higher risk of developing cardiovascular disease. However, the underlying cause of this phenomenon has not yet been investigated. We are aiming to profile the oral microbiome content in patients with heart failure

Oropharyngeal cancer (OPC) caused by human papillomavirus (HPV) is rapidly increasing globally, with an estimated 173,495 new cases in 2018. Approximately ~10-25% of patients develop recurrences within 2-years. The aim of this NHMRC funded project is to develop a microfluidic chip to permit capture of high-purity and viable circulating tumour cells (CTCs) to early detect recurrences in HPV driven OPC.

About 15% of lung cancer patients survive beyond 5-years. CT screening to early detect lung nodules has been investigated, however false positive results, unnecessary radiation exposure are some of the drawbacks. We propose an innovative approach to identify nodules found on CT scans using breath analysis and liquid biopsies. This new multidisciplinary partnership will lay the foundation for future collaborations.

Glioblastoma (GBM) is the most frequent and aggressive form of brain cancer in adults. Currently, there are no biomarkers to reliably evaluate disease progression during treatment, leading to delays in important clinical interventions. To improve noninvasive monitoring of cancer and find new potential targets for therapies, liquid biopsy approaches, including the use of extracellular vesicles (EVs), circulating tumour cells (CTCs) and circulating tumour DNA are being investigated. The liquid biopsy approach has advantages over tumour tissue biopsy since it allows serial timepoints collections and in a minimally invasive way. We aim to expand results obtained on EVs, ctDNA and CTCs isolated from blood and saliva of GBM patients, validating them in larger cohorts and identifying novel biomarkers to help in the diagnosis and prognosis of this disease.

This project harnesses the biosynthesis capacity of microbial cells to produce polymeric self-assemblies that can be engineered to incorporate protein functions such as antigen, binding domains and enzymes. This approach uses metabolic engineering and protein engineering to exploit the vast biomaterials design space for generation of innovative smart materials that form core-shell structures and exhibit advantageous properties toward such as uses as antigen carrier in vaccine applications or for targeted delivery of active compounds.

This project combines advanced protein engineering with materials science and biotechnology. Sensitive and specific detection of serum antibodies is often used to diagnose infections. This project aims to develop a simple qualitative/quantitative device for detection of antibodies of interest. It will involve protein engineering of protein switches to incorporate antigens while attached to biomolecular scaffolds. Binding of the antibodies to the antigens will activate the protein switch which will result in release of a signal.

The spread of cancer (metastasis) accounts for 90% of cancer deaths. Critically, this belligerent disease is highly resistant to conventional therapies, and new molecular targets and therapeutic avenues are urgently needed. Professor Richardson discovered innovative anti-cancer drugs that can increase the expression of a metastasis suppressor protein, NDRG1, that prevents tumour cell spread. He also discovered these same drugs overcome resistance of cancers to chemotherapies by overcoming the drug efflux pump, P-glycoprotein. This project will involve examining the functions of NDRG1 and its targeting by our novel drugs to elucidate the molecular mechanisms involved in their anti-tumour activity. A range of state-of-the-art techniques will be used to maximise student training, including: tissue culture, western blot analysis, immunohistochemistry, medicinal chemistry, and confocal microscopy.

Despite the massive potential of pharmacologically harnessing the power of the macrophage (MØ), a lack of understanding basic molecular mechanisms led to a distinct absence of MØ-based anti-cancer therapies. MØs are powerful orchestrators of the response to tumours, making up to 50% of tumour mass. The MØ powerfully exerts tumour inhibition via either cytotoxic M1-MØs, or tumour promotion via the M2-MØ phenotype. However, a unifying model of how this occurs via nitric oxide (NO) has never been elucidated. Using our expertise in exploiting transporter pharmacology to develop innovative drugs from bench-to-bedside, we will assess the transporter, multidrug resistance-associated protein 1 (MRP1), to exploit NO transport between MØs and tumour cells to develop frontier drugs (“MACA-ATTACKERS”) to harness the immense power of the MØ.

Breast cancer remains a nightmare with the most common breast cancer type, ER(+), being driven by ERα and is also characterised by PR, AR, and PRL-R expression. This project involves a frontier pharmacological strategy to inhibit expression of multiple receptors to prevent breast cancer growth and overcome Tamoxifen resistance. Our agents were developed in our Lab, progressing from bench to bedside, and are designed to address the major challenges in cancer therapy - collectively known as the 'Triad of Death'- through three key mechanisms: (1) overcoming P-glycoprotein mediated drug resistance (2) suppressing metastasis by inducing the potent metastasis suppressor NDRG1; and (3) inhibiting primary tumour growth by targeting oncogenic signalling pathways. The aim of this project is to develop and investigate extensive structure–activity relationships of novel agents that exhibit safe and potent activity, specifically by completely preventing myoglobin oxidation - an issue observed with the clinically trialled DpC.

Peripheral nerve injuries are devastating as they can result in permanent paralysis. This project will use drug discovery and cell transplantation approaches to develop therapies to treat peripheral nerve injuries in animal models. The interaction of the transplanted cells with the host nerve will be examined and the functional outcomes will be addressed using behavioural and electrophysiological studies.

Olfactory glial cell transplantation therapy is effective for repairing spinal cord injury, but the approach needs enhancing to improve outcomes. This project will determine the optimal combination of cell types needed to produce cellular nerve bridges for transplantation into the injury spinal cord. The project will develop new techniques for cell purification and three-dimensional cell nerve bridge production.

Pathogens such as bacteria and viruses are likely contributors to the onset and progression of Alzheimer’s disease. This project identify mechanisms by which microorganisms initiate immune responses leading to chronic inflammation and AD pathologies. The project will use in vitro cell cultures and in vivo animal models of brain infection, or will use human samples.

There are >100 million cases of gonorrhoea/year, and infection can cause severe sequelae including pelvic inflammatory disease, adverse pregnancy outcomes, neonatal complications, infertility, and increased risk of HIV. Gonorrhoea has been recognised by the World Health Organization (WHO) as an urgent threat to global health. There is currently no gonococcal vaccine, and due to multidrug resistance there are concerns that N. gonorrhoeae may become untreatable in the near future. Recent observational studies have found that individuals vaccinated with meningococcal serogroup B (MenB) vaccines are less likely to contract gonorrhoea. We are now conducting a randomised control trial to test the efficacy of the 4CMenB vaccine against gonorrhoea. Human serum samples from this trial will be assessed to understand the vaccine-induced immune response and determine whether antibodies raised to 4CMenB can kill N. gonorrhoeae or block its adherence to host epithelial cells.

Neisseria gonorrhoeae, the causative agent of gonorrhoea, is a significant health problem worldwide. The control of gonorrhoea depends on the development of a vaccine due to the continuing increase of antibiotic resistance and the staggering outcomes of infection, including infertility and increased transmission of HIV. This project aims to characterise potential vaccine candidates to aid in the development of a gonococcal vaccine. The distribution of the identified vaccine candidates will be investigated in a diverse range of N. gonorrhoeae strains. The functions of candidates will be examined by generating a mutant strain of N. gonorrhoeae that does not express the vaccine candidate, and comparing the wild type and mutant strains in a panel of antimicrobial stress assays. The vaccine potential of these candidates will be assessed by testing the ability of antibodies to the vaccine candidates to mediate killing of N. gonorrhoeae or block colonisation of human epithelial cells.

Human mucosal surfaces, such as the airway, contain carbohydrate structures (glycans) and many bacteria have evolved carbohydrate-binding proteins that enable infection of host cells. Our aim is to identify glycans that host-adapted bacterial pathogens bind to during colonisation and disease. This project will focus on bacteria including Neisseria gonorrhoeae (causes gonorrhoea) and Neisseria meningitidis (causes sepsis and meningitis). We will probe Glycan Arrays (consisting of >400 sugars immobilised onto glass-slides) using recombinant proteins and wild type bacteria and a series of mutant strains lacking key outer membrane structures. The affinity and kinetics of interactions will be investigated using surface plasmon resonance. We will also use epithelial cell adherence and invasion assays to investigate the functional role of glycan-based host-pathogen interactions. These findings will contribute to understanding key bacterial and host factors involved in colonisation and disease, and may direct development of new drugs and vaccines for these bacteria.

The natural habitat of campylobacters is the intestine of warm-blooded animals, and therefore chemotactic motility is an important mechanism involved in the colonisation and pathogenicity of this microorganism. Bacterial motility and chemotaxis are subject to sensory control mechanisms that introduce a bias into the swimming direction of the organism towards beneficial environments and away from unfavourable conditions. Chemotaxis sensory receptor proteins play a key role in pathogenicity of this organism as the first line of bacterial – host interaction and thus provide rational targets for the design of novel antimicrobial agents. This project involves characterisation of interactions of the chemoreceptors of C. fetus, named Tlps, with periplasmic ligand-binding proteins The major aim of this project is to identify how periplasmic ligand binding proteins induce directed bacterial motility to nutrients and host targets though small molecule arrays, chemotaxis assays, systematic mutagenesis followed by analysis using mammalian cell culture and animal models.

Natural habitat of campylobacteria is the intestine of warm-blooded animals, and therefore chemotactic motility is an important mechanism involved in the colonisation and pathogenicity of this microorganism. Although chemotaxis has been demonstrated for Campylobacter the chemical substrates, mechanisms involved in the sensory control of motility and the role of chemotaxis in disease, are poorly understood. We, therefore hypothesise that the chemosensory receptor proteins play a key role in chemotaxis and are involved in the pathogenicity of this organism as the first line of bacterial – host interaction and thus provide rational targets for the design of novel antimicrobial agents. This project involves characterisation of interactions of chemoreceptors of C. jejuni, secondary sensors, periplasmic binding proteins and environmental molecules. This will be determined using site-specific mutagenesis followed by analysis of the wild type and mutated proteins using small molecule and glycan arrays, chemotaxis assays and mammalian cell culture.

The natural habitat of campylobacters is the intestine of warm-blooded animals, and therefore chemotactic motility is an important mechanism involved in colonisation and pathogenicity of this microorganism. Bacterial motility and chemotaxis are subject to sensory control mechanisms that introduce a bias into the swimming direction of the organism towards beneficial environments and away from unfavourable conditions. Chemotaxis sensory receptor proteins play a key role in the pathogenicity of this organism as the first line of bacterial – host interaction and thus provide rational targets for the design of novel antimicrobial agents. This project involves characterisation of interactions of the chemoreceptors of C. fetus, named Tlps, with periplasmic ligand-binding proteins The major aim of this project is to identify how periplasmic ligand binding proteins induce directed bacterial motility to nutrients and host targets though small molecule arrays, chemotaxis assays, systematic mutagenesis followed by analysis using mammalian cell culture and animal models.

Calcium Sparks is an intracellular traffic control system that facilitate protein movements. We have recently found that many viral pathogens (including HIV) piggyback onto this cellular process to support their propagation, particular in the process of viral particle formation and release. Using a combination of techniques (such as molecular biology, virology, biochemistry, structural biology and electron microscopy), this project aims to dissect the molecular details underpinning these Ca2+ Sparks-mediated trafficking of viral proteins. In the context of translational science and clinical application, our team has identified several drug candidates to interfere with the reliance of HIV to use this calcium-based system to propagate as well as evaluate these lead compounds in targeting HIV latently infected cells for the eradication of HIV. This project has both PC2 and PC3 components, and they complement each other. The PC3 component incorporates live infectious materials or PC3 related genetic modified organisms will be used.

Our lab has made a paradigm shifting discovery that interactions between a specific pair of glycans are vital to potentiate HIV-host cell attachment. This strategy increased the HIV “dwell time” on the host cell surface, facilitating interaction with receptors for virus entry. The building blocks of glycans are shared across the domains of life. With funding support from U.S. National Institutes of Health, we explore how virus, bacteria, and animal host interact with each other. Specifically, we focus on how glycan-based interaction from bacteria (microbes) may offer protective benefit to human hosts. This work utilises a combination of techniques for investigation, including molecular biology, virology, biochemistry, structural biology and electron microscopy. This project has both PC2 and PC3 components, and they complement each other. The PC3 component incorporates live infectious materials or PC3 related genetic modified organisms will be used.

A major gap in the clinical diagnosis of infectious diseases is the lack of an accessible yet simple assay that possesses both the speed of rapid antigen test (RAT) and the accuracy of lab-based polymerase chain reaction (PCR). By modulating CRISPR (clustered regularly interspaced short palindromic repeats) detection, our purpose-built QuoCuRNA assay is a rapid (<30min), cheap ($0.61 USD per assay) nucleic acid monitoring tool with PCR accuracy. Our QuoCuRNA detection can be adapted to detect any unique nucleic acids sequences with discrimination power as single nucleotide accuracy, making it possible to track the presence of specific pathogen sequences in patients. We will use QuoCuRNA to develop rapid diagnostics against virall pathogens. This work requires: molecular biology; biochemical analyses; RNA biology, protein biochemistry, and dynamic structural biology to dissect mechanism, and collaboration with artificial intelligence scientists to improve efficiency of the system. This project is PC2 based.

Viruses can affect health of agricultural- and wild-animals. This proposal is designed to tackle virus infection in ɫ����վn agricultural animal (such as pig) and ɫ����վn iconic animal species Koala. To protect ɫ����վn pork industry, we will focus on developing a novel vaccination strategy known as ‘Virus-Mimic’ to enable simple delivery of relevant vaccination antigen to target animals to elicit protective immune response against important pig pathogens, such as African Swine Fever Virus (ASFV) and Japanese Encephalitis Virus (JEV). We will establish rapid diagnostic using our QuoCuRNA platform, bypassing the need of expensive lab-based PCR test yet with similar accuracy. In the context of Koala, we will set up koala retroviruses (KoRVs) QuoCuRNA detection, thereby enabling early identification of Koala that are prone to animal diseases. This project is PC2 based that is suitable to researchers at all levels..

Genetic factors constitute a major component in the aetiology of Parkinson's disease (PD). Significant progress towards understanding the pathologic mechanisms involved in PD and developing new therapeutics has come from studies of rare families with inherited PD. We hold an advantaged position in this research field via access to the unique cohort of thousands of PD patients participating in the Queensland Parkinson’s Project. Through sophisticated genetic studies, we have identified several novel genes from rare PD families, the encoded proteins of which have great potential in elucidating new pathologic mechanisms and providing novel treatment strategies. Using methods in molecular biology, cell biology, biochemistry and stem cell biology, we aim to shed new light on this progressive and devastating disease.

Parkinson’s disease (PD) is a complex, incurable, multifactorial neurological condition affecting over 65,000 ɫ����վns with an economic burden of $10 billion per annum. With an aging population the disease related costs will rise unless we find better ways to identify those at risk, provide early diagnosis and treat the disease from an understanding of its causation in each individual. The development of robust biomarkers is essential to meeting these challenges. No biomarkers are available which is the major impediment to progress towards a cure. We have developed a cell model of PF using patients’ own cells. Subjecting the cells to chemical stress reveals a different response between cells from PD patients and those from healthy individuals. We have several projects examining how we can use these stress tests to identify the underlying disease trigger in each patient. This is the first step toward personalised medicine for PD.

Mushrooms are increasingly attracting attention for their immuno-modulatory activities, which are primarily due to beta-glucans. beta-Glucans are chemically diverse glucose (Glc) polysaccharides, with non-cellulosic beta-glucans, mainly beta-(1,3)-linked Glc being shown to be potent immunological stimulators in humans. Mushroom beta-glucans are clinically used in China and Japan, as well as being commercially available in ɫ����վ. Due to the complexity of beta-glucan chemistry and structure a detailed understanding of the mechanism of action, specifically the structural components that dictate specific immunological responses, are yet to be fully resolved. This project aims to structurally characterise mushroom beta-glucans and correlate this with their associated immuno-modulatory effects. The outcomes of this project will lead to a clearer understanding of the properties of beta-glucans associated with commercially available mushroom polysaccharides that induce specific immuno-modulatory effects.

The opportunistic human pathogenic fungus Aspergillus fumigatus causes severe systemic infections including Invasive Aspergillosis (IA), a major cause of life-threatening fungal infections in immuno-compromised patients. An over-whelming number of reports appeared in 2020 demonstrating that COVID-19-associated pulmonary Aspergillosis (CAPA) is one of the leading factors affecting morbidity in critically ill COVID-19 patients with some reports even classifying Aspergillosis as a significantly under-recognized ‘Superinfection’ in COVID-19. Drug resistance among fungal pathogens is continuing to develop into an increasingly serious threat to public health and health-care systems worldwide. This PhD projects entails the development of novel antifungal therapies that are urgently needed using our established and unique combined in-silico/SPR drug discovery pipeline evaluating a number of new protein targets.

Micro-technologies in the form of Micro-Electro-Mechanical Systems (MEMS) and micro-plasmonics platforms offer the potential for high-resolution, high-throughput label-free sensing of biological and chemical analytes. Silicon carbide (SiC) is an ideal material for augmenting both MEMS and plasmonics routes, however such inorganic 2D surfaces need to appropriately and efficiently functionalised to allow subsequent immobilisation of functional biomolecules. To this end we have now developed an affordable, facile one-step method using organosilanes To functionise 2D structure, making label-free MEMS or plasmonic systems possible. Using a similar functionalisation route, we have extended the use of organosilanes to biofunctionalise the surface of 3-dimensional nanoparticles, specifically carbon dots. Carbon dots are cheap, biocompatible, chemically stable, heavy-metal free quantum dots, of low toxicity that offer an alternative approach for bio-imaging and -sensing. We are now using our biofunctionalisation approach to generate glycan-coated carbon dots that we are using to explore complex glyco-interactions.

This project will study the molecular mechanisms used by pathogenic streptococci to avoid killing by metal stress. This project utilizes elements of molecular microbiology, immunology, proteomics, and genetics to better understand how bacterial disease develops in the human host through colonization and evasion of immune responses. Experience in genetic manipulation in bacteria or working with animal models would be an advantage. This project includes self-directed research and components of teamwork and collaboration.

Nature provides unlimited inspiration for innovation in the pharmaceutical and agrochemical sector. The Nobel Prize-winning discovery of the anti-parasitic drugs avermectin and artemisinin has renewed interest in exploring natural products for new anti-infective drugs. This project will result in the identification, semi-synthesis and full characterisation of new molecules that display anti-viral, anti-microbial or anti-parasitic activity.

Humans have utilised plants since the dawn of time for therapeutic purposes. Many important and well-known drugs (e.g. taxol, morphine) come from plants. Endemic ɫ����վn rainforest and desert plants have yielded many new and bioactive natural products, but remain under-investigated. This project will result in the purification and characterisation of new bioactive compounds, and that will impact biodiscovery.

Natural products display chemical complexity and diversity and they inherently interact with biomolecules (e.g. proteins, DNA), making them an ideal source of unique scaffolds for screening library synthesis. This medicinal chemistry project will generate unique biodiscovery libraries that will be fully characterised using spectroscopic methods before being screened in anti-infective, anti-cancer, or ion channel functional assays.

The inclusion of probiotics in animal feeds have proven to be beneficial to animal health. This project, a collaborative research program between Griffith University and Bioproton, aims to discover new probiotic strains from marine microbes and to invetigate their mechanism of action using NMR metabolomics.

Many TCMs have a neuroprotective effect; that is, they protect the central nervous system against damage or degeneration due to diseases such as Parkinson’s disease. Working with TCMs with a known neuroprotective effect, we can isolate and identify the major constituents of selected TCM and test the compounds against cell-based models of Parkinson’s disease. By analysing and testing TCMs, we can determine their mechanism of action and develop new ways to treat neurological diseases.

Lymphoma is the most common lymphoid malignancy and is among the 10th most prevalent cancers worldwide. Non-Hodgkin’s Lymphoma (NHL) accounts for 80–85% of all lymphomas, including the common B-cell NHLs (B-NHLs). Current standard of care for relapsed/refractory NHLs are anthracyclines that are associated with cumulative cardiotoxicity with limited repeated clinical use. Rituximab-based therapy relies on complement and antibody dependent cell-mediated cytotoxicity to effect cell killing and is associated with severe side effects and in some cases form tumour lysis syndrome. This PhD project aims to develop novel therapies with an alternative mechanism for B cell killing and improved outcome by synthesising novel carbohydrate-based ligands and conjugating ligands to toxin-loaded liposomes.

The opportunistic human pathogenic fungus Aspergillus fumigatus causes severe systemic infections including Invasive Aspergillosis (IA), a major cause of life-threatening fungal infections in immuno-compromised patients.  An overwhelming number of reports appeared in 2020 demonstrating that COVID-19-associated pulmonary Aspergillosis (CAPA) is one of the leading factor affecting morbidity in critically ill COVID-19 patients [2] with some reports even classifying Aspergillosis as a significantly under-recognized ‘Superinfection’ in COVID-19.  Drug resistance among fungal pathogens is continuing to develop into an increasingly serious threat to public health and health-care systems worldwide. This PhD projects entails the development of novel antifungal therapies that are urgently needed using our established and unique combined in-silico/SPR drug discovery pipeline evaluating a number of new protein targets. 

The decade long overuse of antibiotics in poultry agriculture and consequently the transferral of antibiotic resistance to humans and the associated health problems underlines the urgent need for novel antibiotic-independent strategies, such as feed supplements (prebiotics) that augment commercial poultry performance and provide food safety. This PhD project aims to develop prebiotic treatment options to reduce the colonisation of Campylobacter jejuni in the chicken intestinal tract. Structural and biophysical investigations of glycan-glycan interactions followed by monitoring the bacterial load in chickens and potential cross-contamination into chicken will form the main part of the thesis. Expected outcomes will be the development of a potentially commercially viable non-antibiotic treatment option for poultry farmers in ɫ����վ.

Red imported fire ants pose a significant threat to ɫ����վ’s environment and economy, yet their communication systems remain poorly understood. This project aims to investigate how fire ants communicate and organise their colonies through chemical signalling. By studying the 3D structures of odorant-binding and chemosensory proteins, we aim to identify new chemicals capable of binding to these proteins and potentially disrupting the ants' communication systems. The outcomes of this research will advance knowledge in entomology and structural biology while offering innovative strategies to manage fire ants, protecting ɫ����վ's vulnerable ecosystems and mitigating substantial economic losses caused by this invasive species.

Ovarian cancer is one of the most lethal gynaecological malignancies, with high mortality largely due to late-stage diagnosis and widespread metastasis. High-grade serous cystadenocarcinoma (HGSC), the most common and aggressive subtype, accounts for the majority of ovarian cancer deaths and is characterised by early peritoneal dissemination. Consequently, there is an urgent need for novel therapeutic options, in particular anti-metastatic treatments. Lewis Y antigen (LeY), a difucosylated glycan, is highly overexpressed in ovarian cancer, linked to poor prognosis and reduced overall survival; however, its role in metastasis remains poorly defined. Most studies have provided only correlative evidence, without uncovering how LeY actively contributes to disease progression. Our preliminary data show that LeY can form high-affinity dimers, structurally stable, sandwich-like complexes with binding energies comparable to DNA base pairing and significantly stronger than most protein-glycan interactions.

Klebsiella pneumoniae, a WHO priority 1 pathogen, causes severe hospital- and community-acquired infections. The emergence of carbapenem-resistant strains has increased mortality and healthcare costs globally, highlighting the need for novel antimicrobial strategies. This project targets the enzyme UDP-galactopyranose mutase (UGM), essential for LPS O-antigen biosynthesis in bacteria but absent in humans, making it a selective and druggable target. Our UGM inhibitors, originally designed for Aspergillus fumigatus, show potent activity against K. pneumoniae, including restoring meropenem efficacy in resistant strains. We aim to optimise lead compounds (Aim 1), understand binding mechanisms via structural and biophysical studies (Aim 2), and validate therapeutic potential using in vivo and ex vivo lung models (Aim 3). AI-based discovery will support identification of new UGM inhibitors. The project leverages a globally recognised, interdisciplinary team and collaboration with Fraunhofer ITEM. Outcomes will validate UGM as a target and deliver urgently needed treatments against resistant bacterial pathogens.

With no approved treatments available, neurotropic Orthoflaviviruses, such as Zika virus and Japanese Encephalitis virus, pose a significant global health threat. These viruses manipulate peroxisomes and lipid droplets, that are involved in nerve cell myelination, leading to severe pathological disorders of the brain and nerves. This project will investigate the molecular mechanisms involved to identify novel therapeutic targets to prevent and/or treat Orthoflavivirus-induced brain damage.

Alphaviruses like Chikungunya, Mayaro, and Ross River are mosquito-borne viruses causing joint pain through inflammation and extracellular matrix (ECM) degradation in cartilage. Chondrocytes and fibroblast-like synoviocytes, which maintain cartilage and produce synovial fluid, are key contributors to this process. Our preliminary findings show RRV infection upregulates CCL5 in these cells, which may increase matrix metalloproteinases and reduce ECM components like proteoglycans. This study aims to investigate CCL5’s role in alphavirus-induced ECM damage and determine if other alphaviruses share similar effects, potentially identifying new targets for therapeutic intervention.

Zoonotic pathogens pose major threats including catastrophic social and economic impacts. Zoonotic infections are triggered by the ability of a pathogen to cross from animal to human. Bats have been shown to carry more than 200 viruses and a significant proportion of these viruses are zoonotic however very little is known about what makes bats unique hosts. This project aims to investigate the mechanisms of viral host interactions focusing on viruses of pandemic potential. Utilising innovative glycobiological technologies this research seeks to be the first ever to identify the “natural” glycome of the bat leading to better prediction and understanding of why bats are uniquely susceptibility to a multitude of important zoonotic viruses.This will fill a significant gap in our knowledge of bat physiology and the unique nature of bats in harbouring viral infections.

Mosquito-transmitted viruses (arboviruses) cause a range of clinical manifestations including encephalitis, arthritis, arthralgia and myalgia. Viruses in this group include the arthritogenic chikungunya virus (CHIKV), Ross River virus (RRV) and the deadly Japanese Encephalitis virus (JEV). With climate change and increasing globalisation, emerging arboviruses such as JEV, CHIKV and RRV are all examples of viruses which could follow Zika and be the next pandemic. Combatting mosquito-borne diseases is one of our most pressing global health challenges. We recently demonstrated that viral-induced disease is largely driven by activation of the host innate inflammatory response. We now aim to define the mechanisms underlying this inflammatory-mediated pathology. This project (backed by a prestigious NHMRC Synergy grant) will identify new host targets for the rapid development of innovative therapies against arboviruses of pandemic potential.

Ulosonic acids are a family of higher order sugars associated with a number of human diseases. Keto-deoxy octulosonic acids are key components of the outer membrane of Gram-negative bacteria, whilst nonulosonic acids include the sialic acids, pseudaminic acids, and legionaminic acids, all of which are known to be associated with human disease and bacterial virulence. In order to expand our understanding of the role of ulosonic acids in human disease, this project will expand on our preliminary work into developing new highly efficient synthesis of ulosonic acids. Students undertaking this project will learn modern synthetic chemistry methodology in state-of-the-art chemistry research laboratories. They will also gain “hands-on” experience with the use of high field NMR spectroscopy and will produce compounds that will ultimately be used as biological probes.

Butenolides are naturally occurring molecules characterised by a central 5-membered lactone ring. There is vast structural diversity and biological activity within this group of naturally occurring compounds. We have recently developed a highly efficient and flexible synthesis of this important class of natural products. This project will focus on expanding our synthetic chemistry method to allow the synthesis of novel butenolides, and then evaluate the synthesised compounds for their biological activity. Currently we have research looking at the anticancer activity of butenolides, but this can be expanded to include antimicrobial activity as well.

Natural products represent an important source of novel chemical entities with unique biological activity. This project involves the synthesis of compounds that are structurally related to specific classes of natural products that have biological activity (e.g. anticancer activity). The aim of the synthetic chemistry is to provide novel compounds with potentially improved pharmacological profiles in comparison to the natural compounds. The specific types of compounds to be made will be determined upon discussion with the student. Students undertaking this project will learn modern synthetic chemistry methodology in state-of-the-art chemistry research laboratories, as well as gaining hands-on experience with a number of important spectroscopic instrumentation.

The World Health Organization ranks carbapenem-resistant Acinetobacter baumannii as the highest critical priority bacterial pathogen for therapeutics development due to a lack of effective antibiotics. Promising new therapeutic options include strategies that target the polysaccharide capsule (CPS) on the cell surface, such as bacteriophage therapy. This research project will focus on extending our understanding of key enzymes that determine CPS structure and function, and the role that bacteriophage play in influencing enzyme specificity. This is expected to provide novel insights into the clinical impact of CPS diversity, as well as its subsequent implications for phage therapy, immunotherapy and vaccine research. This project will utilise molecular microbiology and genetics techniques, including the handling of multidrug resistant bacterial pathogens, as well as genomics and bioinformatics methodologies.

Infectious diseases cause over 17 million deaths annually. SARS-CoV-2, the virus responsible for COVID-19, has caused over 5 million deaths globally. While several vaccines have been approved, their efficacy against emerging variants remains uncertain. Streptococcus pyogenes, another major pathogen, causes a range of diseases including rheumatic fever and rheumatic heart disease, resulting in over 500,000 deaths annually—yet no licensed vaccine exists. Our laboratory focuses on rational vaccine design using peptide antigens and targeted delivery systems. For S. pyogenes, we have developed peptide-based vaccines combined with adjuvants or liposomal carriers, two of which are currently in Phase I clinical trials. In parallel, we are developing a peptide-based SARS-CoV-2 vaccine targeting the receptor-binding domain of the Spike protein, aiming for broad protection, including at mucosal surfaces. This project aims to generate safe, effective, and broadly protective vaccines with real-world impact against both bacterial and viral respiratory pathogens.

Seemingly mild Streptococcus pyogenes infections can rapidly progress to invasive disease (ISD), with fatal outcomes. ISD incidence ranges from 2–4 per 100,000 in developed countries, but rates can reach 75 per 100,000 in vulnerable populations in low-resource settings. Around 20% of ISD cases are complicated by streptococcal toxic shock syndrome (STSS), a severe condition marked by multi-organ failure and high mortality, even with advanced care. While streptococcal superantigens (SAgs) like SpeA and SpeC are known contributors, we have shown the M protein also plays a critical role in STSS pathogenesis. Using HLA-humanised mice, we modelled STSS and aim to extend this work to examine disease caused by SpeA+ strains. This project will evaluate whether monoclonal antibody to our lead vaccine candidate (J8/p*17) can treat STSS. We will also explore combination therapy with antibiotics, aiming to reduce antibiotic usage and resistance. This work could fast-track novel treatment strategies for STSS.

Streptococcus pyogenes is a Gram-positive human pathogen responsible for a wide range of diseases, from mild throat and skin infections to life-threatening streptococcal toxic shock syndrome and rheumatic heart disease, causing over 500,000 deaths annually. Naturally acquired immunity is slow to develop due to immune evasion by virulence factors and the extensive diversity of the M protein, with over 250 serotypes. This antigenic variability presents a major challenge for vaccine development, as protection must extend across diverse strains and both primary infection sites—skin and mucosa. This project aims to investigate immune responses following natural infection and vaccination in mice and humans, focusing on whether infection or immunity at one site (e.g. skin) can confer protection at another (e.g. mucosa). By dissecting the role of specific immune cell populations in cross-compartment and site-specific protection, this project will inform the rational design of broadly protective S. pyogenes vaccines and immunisation strategies.

We have developed a number of virus-derived protein cages into robust containers for enzymes. In addition, we are constructing hybrid biomaterials with properties tailored to working with different classes of small molecules. There are a number of project opportunities on the application of biocatalytic protein cages in drug discovery and metabolism.

We have determined the first structure of a persistent plant virus. It is not clear what advantage these asymptomatic viruses confer in order to maintain the purported symbiotic relationship they have with their hosts. Understanding the form and function of persistent viruses through molecular and structural biology will open many possibilities for their use in plant biotechnology.

Virus-like particles are non-infectious mimics of viruses that can often enter cells via the same receptor-mediated pathways as the viruses they resemble. Our work in this area includes the development of fluorescent analogues of important human pathogens and the creation of particles of different shape and size to understand the fundamentals of virus-cell interactions.

The metabolic interactions between the endosymbiont Wolbachia and its insect hosts depend on the combination of Wolbachia strain and host organism and range from mutualistic symbiosis to parasitic interactions. With a combination of metabolomics and physiological techniques we want to characterise these interactions and the role they play in hindering the transmission of insect-borne virus diseases.

Previously we showed the enzyme dihydrolipoamide dehydrogenase (DLD) to be a metabolic master regulator. We now will characterise the role of DLD in the metabolic network of C. elegans by using metabolomics and biophysical techniques in isolated mitochondria, as well as curating the genome scale metabolic model of C. elegans in collaboration with the WormJam consortium and simulating the nematode’s metabolism.

In collaboration with colleagues at QAAFI and other international institutions we are using NMR-based metabolomics as analytical platform technology to characterise the composition of foods such as honey and native ɫ����վn fruits. This involves characterising the potential of native ɫ����վn fruits as commercial food sources and developing methods for the detection of food fraud especially in honey.

Giardia parasites infect approximately 1 billion people and cause over 200 million cases of giardiasis each year. They also cause significant morbidity in animals. However, current treatments are inadequate, associated with resistance and collateral microbiota impacts. This project aims to improve the treatment of giardiasis by investigating the biological and pre-clinical activity of potent new anti-Giardia compounds using state-of -the-art in vitro and animal models of infection.

Trichomoniasis is a neglected parasitic disease that causes significant morbidity in pregnant and elderly women (over 100 million infections each year). However, the only FDA approved therapy for this disease is associated with treatment failures and adverse effects. This project aims to develop and implement a new medium to high-throughput assay to identify and investigate new drug leads for trichomoniasis.

Babesiosis is a tick-borne infectious disease, caused by parasites of the genus Babesia. Human babesiosis is typically asymptomatic, however in the very young, the elderly, and immunocompromised individuals can result in acute anemia, multi-organ failure, or death. Babesia parasites also infect cattle, with bovine babesiosis having a major economic impact on the livestock industry in various countries including ɫ����վ. We have shown that a whole blood-stage parasite liposomal babesiosis vaccine is able to induce protective immunity in rodent models of Babesia microti. Further work is required to optimise the vaccine formulation to maximise protective efficacy and enable the development of a product that is compatible with administration to humans and cattle. In this project, vaccine candidates will be generated containing the whole Babesia parasite. For some candidates, parasite-derived recombinant proteins/peptides will also be included. Pre-clinical development of these vaccine candidates will include characterisation, optimisation and evaluation of the vaccine formulations.

Malaria is a parasitic disease prevalent in 85 countries and is associated with substantial morbidity and mortality. Current control strategies are becoming less effective; therefore the development of a highly effective vaccine is considered to be of critical importance. Using rodent models of malaria, we have shown that different whole parasite asexual blood-stage vaccines are able to induce multi-strain and species protective immune responses. One such approach is controlled infection immunization (CII). This involves administering a malaria infection concurrently with anti-malarial drug treatment. Using rodent models of malaria, this project will involve characterising and optimising different anti-malarial drugs in the context of CII. The optimal drug formulations and parasite combinations will be evaluated for their ability to induce protection against subsequent challenge infection. Immunological/functional assays will be used to assess immunogenicity and examine immune mechanisms of protection. Results from this project will inform transitioning this vaccine approach into clinical studies.

Malaria is a global public health problem with transmission reported in 85 countries. Current control methods are becoming less effective, therefore developing a highly effective vaccine is considered to be of critical importance. This study will involve the pre-clinical investigation of a Plasmodium falciparum transmission blocking liposomal vaccine. In this project, different vaccine candidates will be generated containing the P. falciparum gametocyte-stage parasite; this is the life-cycle stage that is found in the blood of malaria-infected individuals and is infective to mosquitoes. For some candidates, recombinant proteins/peptides derived from the gamete, which is the parasite-stage of the parasite within the mosquito, will also be included. Pre-clinical development of these vaccine candidates will include characterisation & optimisation of the vaccine formulations. Immunological/functional assays will be undertaken to characterise immunogenicity and transmission-blocking activity of vaccine candidates ie whether the induced immune response impacts on parasite development and/or survival in the mosquito host.

Malaria is a parasitic disease prevalent in 85 countries and is associated with substantial morbidity and mortality. Current control strategies are becoming less effective; therefore the development of a highly effective vaccine is considered to be of critical importance. Using rodent models of malaria, we have shown that different whole parasite asexual blood-stage vaccine candidates are able to induce multi-strain protective immune responses. In these studies, protective immunity was evaluated using thick blood films to measure peripheral parasitemias. We are now interested in assessing the efficacy of our vaccine candidates using total parasite bioburden as an endpoint. This will involve challenging vaccinated mice with luciferase-expressing P. yoelii parasites and undertaking whole body quantitative bioluminescent imaging. These results will be compared with peripheral blood parasitemias. This data will inform the clinical development of our whole parasite blood-stage vaccine candidates.

Techniques from physics have often been adapted to solve problems in the life sciences. Notable examples include microscopy, x-ray diffraction, and fluorescent labelling. We are interested in developing new ways to investigate the properties of cells, subcellular structures, and large biomolecules using ion trapping techniques from quantum physics. Project students will be involved in a subset of the following project aspects: culturing and fluorescent labelling of yeast cells, loading yeast cells into an ion trap, and then measuring the physical properties and manipulating the cell using electrical, hydrodynamic, and laser methods. There are also projects available on mathematical modelling of the particles. Physics or Biological laboratory course experience preferred for in-lab components.

Axon loss is a common theme in some of the most prevalent neurological diseases, including peripheral neuropathies, traumatic brain injury, Parkinson’s disease and glaucoma, but no treatments currently exists that effectively target axonal breakdown. The protein SARM1 is a central player in axon loss. In healthy nerve cells, SARM1 (sterile alpha and TIR motif 1) is present but inactive. Disease and injury activate SARM1, which results in rapid breakdown of the essential “helper molecule” nicotinamide adenine dinucleotide (NAD+) and ultimately destruction of the axon. We have demonstrated that it is SARM1 itself that cleaves NAD+ upon activation through self-association and we hypothesise that detailed structural knowledge of the SARM1 catalytic mechanism and defining the molecular mechanisms upstream and downstream of SARM1 enzyme activity can yield inhibitors as leads to anti-neurodegenerative disease therapeutics. This project can include work in one, or several, areas, including Cryo-EM, X-ray crystallography, NMR and inhibitor design. 

Nucleotides play important roles in activation of plant immune responses to prevent pathogen infection and are therefore potential targets for development of crop protecting agents. This project aims to use chemical and structural biology approaches to develop stable and cell-permeable small molecules that can be used as chemical probes to study plant immunity and develop new strategies to protect crops from disease and invasive plants. This project can include work in one, or several, areas, including medicinal and organic chemistry, fragment and compound screening and cryo-EM/X-ray crystallography.

 Phages effectively destroy bacteria and is being considered as an alternative to antibiotics for treating resistant bacterial infections. A drawback of phage-based treatments is the plethora of immune systems that bacteria use to protect themselves against phages. This project aims to use structural and chemical approaches to increase our understanding of how anti-phage immune systems can be activated and inhibited. The project is expected to unravel general principles of how phages interact with bacterial immune systems and new strategies to fight bacterial infections. This project can include work in one, or several, areas, including cryo-EM, X-ray crystallography, NMR and medicinal/organic chemistry.

Non-typeable Haemophilus influenzae is a major human adapted pathogen, and causes a number of acute and chronic diseases of the human respiratory tract. Invasive disease caused by NTHi is increasing annually, and is a particular problem in infants under 1 year of age, where the mortality is close to 20%. Antibiotic resistance is increasing each year, resulting in NTHi being on the World Health Organisation’s list of priority pathogens. There is no currently licensed vaccine available for NTHi. Vaccine design is a problem for NTHi as individual strains show high genetic diversity. This project will identify and study new antigenic targets to include in a rationally designed vaccine against NTHi.

Acinetobacter baumannii is classified by the WHO as a top priority pathogen, and is resistant to almost all current antibiotics. The rate at which A. baumannii acquires resistance to antibiotics means A. baumannii infections may soon become impossible to treat, meaning development of novel treatment methods is critical. Although much is known about A. baumannii virulence factors, little is known about the exact host factors A. baumannii interacts with during colonisation and disease. This project will determine which glycans A. baumannii interacts with in the human host, i.e., we will define the A. baumannii glycointeractome, and explore ways of blocking these interactions, to serve as an alternative to, or act in synergy with, existing antibiotics. 

Non-typeable Haemophilus influenzae (NTHi) and Streptococcus pneumoniae (Spn) both colonise the human nasopharynx asymptomatically. Transition to other niches in the body results in a variety of diseases, such as middle ear infection (otitis media, OM) in young children, exacerbations in chronic obstructive pulmonary disease (COPD) in the elderly, pneumonia across all age-groups, and serious invasive diseases, such as sepsis and meningitis. Co-infections involving the two species are common, but the molecular basis for their interactions is poorly understood. Our studies will investigate NTHi-pneumococcal interactions in vitro to determine how these two species cause disease. This information will allow development of better vaccines and treatments for two pathogens of major importance to human health

Neurological disorders such as schizophrenia and dementia are caused by a ‘perfect storm’ of unique combinations of genetic and environmental factors. Such complex combination of events leads to disruptions in gene networks and biological pathways that alter cell functions and consequently influence disease risk. New approaches in genomic technologies, computational models and experimental systems could potentially lead to personalised treatment based on an individual’s genetic composition. This project aims to map molecular networks and cell functions affected in patient-derived stem cells to help discover new therapeutic strategies tailored based on patient’s molecular and cellular signatures.

This project aims to investigate the epigenetic regulation via microRNA gene silencing adopted by Epstein-Barr virus (EBV) to “hack” the genetic program of human B-lymphocytes (B-cells). We use a novel EBV/B-cell model system to characterise the functional role of viral microRNAs in the micro-management of cellular pathways associated with persistent B-cell infection. Our integrated platform will contribute to better understanding of fundamental molecular and cellular processes underpinning viral infection, immune escape and proliferation. The overarching goal is to produce a system-based platform to understand the mechanisms of epigenetic regulation by microRNA gene silencing associated with virus-host interactions and human cell infection.

Carbohydrate binding proteins (also known as lectins) are a broad range of proteins with a wide specificity for carbohydrate structures. Recently we have found that a large number of bacterial and eukaryotic proteins have the ability to bind to glycans that had not previously been appreciated. This project will investigate a range of proteins from bacterial and eukaryotic sources for their ability to interact with glycans. This study will utilise the glycomics arrays that we produce within the Institute for Glycomics as well as studies of affinity and kinetics using surface plamon resonance (GE Biacore T100) and micro isothermal calorimetry (TA Instruments nanoITC). 

The cholesterol-dependent cytolysins (CDCs) are a family of toxins produced by a number of Gram-positive human pathogens including Streptococcus, Clostridium, Listeria, Bacillus and Gardnerella. These toxins form pores in cholesterol-containing membranes, hence it was thought that cholesterol was the cellular receptor. We have found that the CDCs bind with high-affinity to glycan targets and that these glycans serve as cellular receptors. This project aims to further investigate the glycan binding of the CDCs by using molecular modeling to identify key residues involved in binding to the glycan targets. Site-directed mutants of these residues will be generated and analysed using a range of techniques, including surface plasmon resonance (SPR) and cell-based assays, to confirm their role in glycan binding. Identifying key residues of the CDCs involved in glycan binding will provide insight into the function and tropism of these toxins and may assist in the development of inhibitors of these toxins.

Approximately half of all human proteins carry carbohydrates through the process of glycosylation. Glycosylation in cancer cells is altered, including differential expression, truncation and the appearance of novel glycans. Glycans, therefore, make ideal cancer biomarkers because these molecules are shed into the circulation allowing them to be detected in serum. Glycans terminating with the sialic acid Neu5Gc are not expressed at significant levels on healthy human tissues but are in higher abundance on tumour tissue and secretions. The engineered toxin subunit SubB2M has specificity and selectivity for Neu5Gc containing glycans. We showed that SubB2M can detect elevated levels of Neu5Gc in serum samples of multiple cancers via surface plasmon resonance (SPR). SPR is a technique for measuring the label-free binding of molecules in real-time but cannot inform us of the molecule carrying Neu5Gc. This project will aim to discover and characterize Neu5Gc-containing cancer biomarkers from one or more cancer types.

We are currently seeking a highly motivated PhD candidate to undertake research in the development of single-cell glycomics methodologies using mass spectrometry. The primary focus of this project is to establish a technologically advanced platform for the detailed mapping of glycans at the single-cell level, using both tissue-derived and suspension-derived cells. This work aims to address key challenges in the field of functional glycomics and contribute to a deeper understanding of cellular heterogeneity in health and disease.

This PhD project focuses on the development and application of advanced spatial multi-omics approaches to comprehensively profile breast cancer at the molecular level. The project will integrate mass spectrometry-based spatial proteomics (both LC-MS/MS and MALDI imaging), spatial metabolomics, and glycomics to elucidate the complex tumour microenvironment and molecular heterogeneity of breast cancer. Additionally, high-throughput shotgun proteomics will be employed to identify potential therapeutic targets and enable the stratification of patients for personalised treatment approaches. This multidisciplinary project aims to advance precision oncology by bridging cutting-edge mass spectrometry techniques with translational cancer research.

This project aims to develop a cancer-agnostic, multiscale morphological framework by integrating spatial omics with advanced computational pathology. Using mass spectrometry-based imaging (e.g., MALDI imaging), histology, immunohistochemistry (IHC), and spatial transcriptomics, we will generate high-resolution, multimodal data to characterise tumour heterogeneity from molecular to microscopic scales. The goal is to identify diagnostic and prognostic signatures across cancer types by aligning and analysing these datasets using deep learning models, including contrastive learning techniques. These models will extract integrated features from each imaging modality, enabling the classification of tumour phenotypes and the identification of clinically relevant subgroups. This interdisciplinary project brings together expertise in mass spectrometry, molecular pathology, artificial intelligence, and clinical oncology. It offers a unique opportunity to contribute to the development of next-generation diagnostic tools and precision oncology strategies by building robust, generalisable frameworks for cancer classification and patient stratification.

The aim of this project is to isolate surface carbohydrate components from bacteria in the Moraxellacae family, then determine the structures and biological significance of these carbohydrate molecules. Many bacteria in this family are commensals of the human upper respiratory tract and are important in protecting against disease. Obtaining structural carbohydrate information will enable us to determine the role of these carbohydrates, and potentially developing new strategies to promote upper-respiratory tract health. The project will require the development of knowledge and skills in the areas of cell culture, chemical and biochemical extraction and manipulation strategies, nuclear magnetic resonance (NMR) and mass spectrometry (MS) of isolated carbohydrate materials. 

From previous studies, it is clear that the use of Proteolysis-targeting chimera (PROTAC) molecules can result in the effective degradation of target-proteins. PROTAC techniques involve the exploitation of normal protein degradation essential for cellular maintenance and hijacking the system to specifically target proteins of interest (POI) for degradation.  To achieve an effective PROTAC design the molecule must provide high affinity binding to both the protein of interest and a suitable ubiquitin ligase, and maintain these interactions whilst not inhibiting the overall ubiquitination (or tagging for destruction) process. Work is underway within the Institute for Glycomics to synthesise novel PROTAC molecules to achieve the successful proteolysis of a cancer-associated protein, which is known to be intimately involved in cancer progression. This research will be further progressed in this ongoing project.

Nontypeable Haemophilus influenzae (NTHi) causes middle ear infections in children, sinusitis in adults, acute bronchitis, and exacerbations of chronic obstructive pulmonary disease. Neisseria gonorrhoeae (Ng) infects human mucosal surfaces, leading to the sexually transmitted infection gonorrhea. Both pathogens are increasingly resistant to antimicrobial treatments, posing major public health challenges. No vaccines currently exist for either bacterium, and both have evolved immune evasion strategies that complicate vaccine development. Our laboratory has identified a common feature in NTHi and Ng, as both exhibit abnormal expression of a unique surface sugar. This project aims to design and synthesize multiple versions of carbohydrate-based vaccine antigens targeting this shared sugar structure. These candidates will be tested in mice for their protective efficacy. If successful, this work could lay the foundation for clinical development of a novel vaccine, offering a much-needed strategy for the prevention and treatment of infections caused by NTHi and Ng.

Antimicrobial resistance is a growing concern in pathogenic Neisseria species such as Neisseria gonorrhoeae and Neisseria meningitidis, which are responsible for causing sexually transmitted infections and meningitis, respectively. These bacteria have developed resistance to multiple antibiotics, including penicillin, tetracycline, and fluoroquinolones, which were previously used to treat these infections. The emergence and spread of antimicrobial resistance in pathogenic Neisseria is primarily driven by the acquisition of resistance genes through horizontal gene transfer. This has led to the development of multidrug-resistant strains that are becoming increasingly difficult to treat, posing a significant threat to public health. To address this issue, there is a need for the development of new antibiotics and alternative therapies that are effective against multidrug-resistant strains. The goal of this project is to develop a novel treatment for pathogenic Neisseria.

Neisseria gonorrhoeae is a bacterial pathogen that causes the sexually transmitted disease gonorrhea by infecting male urethral and female cervical tissues. One of the major virulence factors of N. gonorrhoeae is Lipooligosaccharides (LOS), which can take on multiple glycoforms due to the phase variation of the genes involved in LOS biosynthesis. The structure of gonococcal LOS is capped with N-acetyl-5-neuraminic acid (Neu5Ac), but the bacterium cannot synthesize the CMP-Neu5Ac required for LOS biosynthesis and must acquire it from the host. While the core-oligosaccharide of LOS is assembled in the cytoplasm, our published study revealed that the alpha-2,3-sialyltransferase Lst, previously thought to be a surface-exposed outer membrane protein, is located inside the cell. This suggests the existence of a transport system or trans-sialidase to transport CMP-Neu5Ac from the host to inside the cell. The goal of this project is to identify a novel CMP Neu5Ac transporter in Neisseria.

Over 65 years ago, Guinea pig serum was fortuitously discovered as an effective anti-tumour agent against transplantable lymphomas. Subsequent investigations revealed that L-asparaginase in the serum was responsible for this remarkable anti-tumour property. The anti-tumour properties of L-asparaginases are primarily due to the depletion of exogenous L-asparagine, which is crucial for the growth of tumour cells, as they are essentially auxotrophic for L-asparagine. Despite its discovery, the identification of guinea pig serum L-asparaginase has been problematic, as it has only been found in guinea pig serum and not in the sera of other species studied, including mice and rats. This project aims to determine the genetic and biochemical origins of liver and serum enzymes in guinea pigs and related species, which also have both isozymes.

For nearly a century, Campylobacter and Helicobacter, both Gram-negative microaerophilic bacteria, have been recognized as pathogens affecting various animal species. More recently, scientists have shed light on their ability to initiate disruptions within the human stomach. These pathogens are now comprehended as triggers for a diverse spectrum of ailments, spanning conditions like diarrheal diseases, systemic infections, chronic superficial gastritis, peptic ulcer disease, and even their potential implication in the development of gastric carcinoma. Concerningly, these pathogens are exhibiting increasing resistance to antibiotics, which are commonly used to treat Campylobacter and Helicobacter infections in clinical settings. This resistance has led to the emergence of multidrug-resistant strains, posing a significant public health threat. To address this challenge, there is a need to develop new antibiotics and alternative therapies effective against these multidrug-resistant strains. This project aims to develop an innovative treatment approach for Campylobacter and Helicobacter infections.

The cells of blood vessels produce sticky molecules called proteoglycans and once modified can bind and retain cholesterol. Zebrafish express all the major receptors, lipoproteins and enzymes involved in atherosclerosis and a complete set of genes to proteoglycan synthesis and modification. This project will develop a high-fat diet-induced zebrafish model of atherosclerosis to allow for screening of potential vessel wall directed therapies to prevent cholesterol binding.

Well defined risk factors such as high cholesterol, smoking, and high blood pressure worsen the burden of atherosclerosis. Patients with inflammatory bowel disease (IBD) present with a lower prevalence of classic risk factors, however, have at least a 2-fold higher risk of heart disease. Elevated inflammatory cytokines and an altered microbiome are observed in patients with IBD. This project seeks to define the biological link between IBD and heart disease by assessing the role of inflammatory cytokines and bacteria-derived toxins on vascular cells.

The immune system responds to infections after it has recognised infectious agents. All bacteria secrete products, and some of these have profound effects on the host immune system, either acting as recognition molecules for immune attack, or by modifying the immune response to assist the microbe to survive. We are investigating how secreted molecules from pathogenic bacteria are detected. We are characterising host receptors for these secreted products, which will help understand diseases such as cholera, legionnaire’s disease, as well as infections caused by Pseudomonas in burned, and cystic fibrosis patients. 

Antimicrobial resistance is a growing threat to human health, with many multidrug-resistant (MDR) and extensively drug-resistant (XDR) infections only treatable by intravenous “last resort” drugs. Oral delivery is ineffective as these drugs don't easily cross the gut into the bloodstream. Long-term intravenous use, however, increases complications, mortality, and resistance. This project aims to improve oral delivery by using “permeation enhancers” — compounds that temporarily increase gut permeability to allow effective absorption. This multidisciplinary project involves experts across Griffith and offers opportunities to gain skills in chemical synthesis, mucosal cell analysis, antimicrobial efficacy testing, and high-throughput imaging. Future work will explore using permeation enhancers with novel antimicrobials and in targeting other diseases, including neurological conditions and cancer.

Neurodegenerative diseases involve the gradual deterioration of neuron structure and function, often linked to the misfolding and accumulation of specific proteins in the brain. Alzheimer’s disease (AD) is associated with amyloid-β (Aβ) and tau proteins, while Parkinson’s disease (PD) involves α-synuclein (α-syn). Other conditions, such as Huntington’s disease, ALS, and Creutzfeldt-Jakob disease, follow similar patterns. Currently, no cures exist—only limited palliative treatments. Research is increasingly focused on disrupting early-stage protein aggregation, targeting monomers or small oligomers. Promising strategies include peptide-based agents that bind selectively to multiple protein sites and multi-target directed ligands (MTDLs) that address several pathological factors simultaneously. This project aims to explore novel chemical scaffolds that may inhibit protein aggregation. Based on literature insights, we will design and synthesize candidate compounds, then screen them for activity against aggregation of Aβ, α-syn, and prion proteins, with the goal of identifying new therapeutic leads.

Glycoproteins are proteins modified by the attachment of glycans, which influence their structure, stability, and function. Abnormal glycosylation is associated with diseases such as cancer and neurodegenerative disorders. N-glycosylation, involving cell-surface glycans, plays key roles in cell communication, protein trafficking, immune signaling, and disease progression. Studying glycosylation is therefore vital for understanding protein function and developing new treatments. Metabolic chemical reporters—small molecules incorporated into glycans—enable selective labeling and tracking of glycoproteins. This project aims to design and synthesize metabolic reporter reagents that target specific glycan structures, such as sialic acid, glucose, galactose, mannose, and N-acetylglucosamine. These reagents will be used to label glycoproteins through glycan editing, allowing for precise investigation of glycosylation patterns. The synthesized compounds will be tested on various glycoproteins, and their labeling efficiency will be evaluated using analytical techniques, including mass spectrometry, to assess their potential in glycoprotein research.

This research aims to support the global fight against malaria by discovering new drug leads to improve and save lives. Malaria, a parasitic disease, causes around 200 million clinical cases and over 400,000 deaths annually. Despite the availability of antimalarial drugs, the emergence of drug-resistant parasites poses a significant threat, and no broadly effective vaccine currently exists. Therefore, there is an urgent need for new therapeutic options. In this project, chemical compound libraries from the Compounds ɫ����վ facility will be screened against Plasmodium falciparum to identify potential drug candidates. Additionally, compounds will be tested against genetically distinct P. falciparum lines to uncover agents targeting novel biological pathways. Based on literature insights, promising compound scaffolds will be designed and synthesized. These candidates will then undergo structure-activity relationship (SAR) studies to optimize their antimalarial properties and advance the development of effective new treatments.

A number of PhD/Masters/Honours/Capstone projects are available focusing on the development and validation of computational methods for binding free energy calculations including Alchemical relative binding free energy calculations and Linear Interaction Energy (LIE) on a series of benchmark and in-house datasets, combining parameters from the web-server (https://groligff.net), and their applications to a range of collaborative drug discovery projects within Griffith University.

The rigorous and accurate calculation of the relative binding free-energy requires performing an ‘alchemical transformation’ between two ligands, using either thermodynamic integration (estimating reversible work) or free-energy perturbation (estimating relative probability) using conformational ensembles from molecular dynamics simulations of 15-20 intermediate steps, making them computationally expensive. The project aims to develop and optimize free energy calculation methods to be an order of magnitude faster leading to >90% reduction in the carbon footprint of computation.

Deciphering the structure (binding pose) and affinity (binding free energy) of a ligand:acceptor complex is critical to overall drug discovery. There remains uncertainty about the binding pose including tautomeric, protomeric and stereoisomeric state(s) as well as conformation and relative orientation, which could in turn lead to potential failure of drug design efforts. The project aims to develop protocols combining molecular dynamics simulations, quantum mechanical and free energy calculations to identify, correct and validate binding mode(s) of drug-like molecules.

The statics structures of Proteins, the most prevalent biomolecule as a therapeutic target, are often solved using experimental methods including x-ray crystallography. The project aims to apply atomistic molecular dynamics (MD) simulations to understand structure, function and binding of physiologically relevant glycosylated form of therapeutically relevant proteins. The protein targets include, but not limited to, the biding of Stem Cell Factor (SCF) to receptor c-KIT.

Immunopeptidomics—the large-scale analysis of peptides presented by MHC molecules—has transformed our understanding of antigen presentation and immune recognition. However, the role of post-translational modifications, particularly glycosylation, remains underexplored. Glycosylation can significantly influence antigen processing, MHC binding, and T cell activation. Emerging evidence shows that glycosylated peptides are naturally presented by both MHC class I and II molecules and that glycosylation of MHC molecules themselves may affect peptide selection. This PhD project aims to address this knowledge gap by developing mass spectrometry and bioinformatics approaches to sensitively detect and characterise glycosylated immunopeptides. Using cancer and autoimmune disease cell lines and clinical samples, we will map the diversity and prevalence of glycosylated peptides in the immunopeptidome. Functional assays will assess how glycosylation influences peptide binding, stability, and T cell activation. This work will establish new analytical pipelines and uncover novel mechanisms of immune regulation involving glycosylation.

Many biomolecules have an electric dipole moment due to asymmetries in their charge distribution. We're interested in mathematically modelling how this aspect impacts the confinement strength and stability, particularly for large biomolecules, in the context of Paul type ion traps as well as optical traps and the cross-over between these two limits. Joint IBG/QUATRI related project.

Our research group aims to analyse the changes in neuronal activity and brain states in zebrafish model, using a combination of linear regression, clustering and graph theory. Diverse projects in the lab have generated complete datasets of whole brain activity that need to be analysed for publication. Those datasets comprise videos of the recording of the neuronal activity in zebrafish brains undergoing environmental changes or presenting genetic modifications. This project aims to find the differences in terms network, dynamics and brain states between experimental groups. Students will gain skills in coding, knowledge in neuronal network and brain states, skills in oral and writing presentations through reports and group meeting attendance, the opportunity to generate publications from their research. This project is open to applications from students with a background in coding (ideally Matlab but not necessary) and statistical analysis.

Respiratory tract viral pathogens, such as respiratory syncytial virus, influenza virus, human metapneumovirus and parainfluenza virus, lead to significant clinical disease and in some instances both diagnosis and treatment are not readily available. This project will develop novel diagnostics and therapeutics based on nanobody technology. We have developed this technology to target these pathogens, and our preliminary data demonstrates the validity of the approach. This project is multidisciplinary and brings together molecular biology, virology, protein biochemistry, structural biology and antiviral drug discovery techniques to fill significant knowledge gaps that will lead to the discovery of new antiviral agents.

A PhD scholarship is available to develop fragment-derived compounds for the treatment of neurodegeneration. The primary focus of this role is to perform targeted fragment/compound screening and design anti-degenerative compounds based on target-fragment interactions. This project includes both experimental and computational work. Success in this role requires collaboration with structural biologists and neurobiologists.

Cellular energy homeostasis relies on precise sensing of metabolic intermediates. While proteins like AMPK and mTORC1 are established energy sensors, many metabolite-protein interactions remain unexplored. This project aims to identify novel sensor proteins that directly bind key metabolites from glycolysis and the tricarboxylic acid (TCA) cycle. By integrating computational techniques, such as structural modeling and molecular docking, with experimental validation methods like thermal shift assays, the study will uncover proteins responsive to specific metabolites. Subsequent functional analyses will elucidate how these interactions influence cellular signaling and metabolic regulation. The findings will advance our understanding of metabolic sensing mechanisms and may reveal new targets for therapeutic intervention in metabolic disorders.

Nicotinamide adenine dinucleotide (NAD+) is an essential molecule for all cellular life. The primary focus of this project is to perform biochemical and biophysical assays to characterize the molecular functions of such proteins and metabolites, with the possibility of developing small-molecule modulators of NAD+ metabolism and NAD+-dependent signalling pathways. This project includes both experimental and computational work as well as collaboration with molecular biologists, structural biologists, and cell biologists.

Protein folding is essential for proper structure and function, and when this process fails, it can lead to serious cellular issues. Misfolded proteins may lose their biological activity or form harmful aggregates that disrupt cellular function. This project integrates medicinal chemistry with biochemical and biophysical techniques to investigate the molecular mechanisms behind protein misfolding. By exploring how proteins adopt abnormal conformations, the research aims to uncover new insights into the underlying causes of protein aggregation. The expected outcomes include a deeper understanding of neurobiology, particularly in the context of age-related diseases, and the development of innovative technologies inspired by biological self-assembly. These findings could contribute to new therapeutic strategies for conditions linked to protein misfolding, offering long-term benefits in the treatment of neurodegenerative disorders and other age-associated diseases.

This project leverages fluorine-19 nuclear magnetic resonance (19F NMR) spectroscopy to explore applications in biological and pharmaceutical sciences. With its high sensitivity, lack of background signal in biological samples, and wide chemical shift range, 19F NMR offers a powerful platform for studying protein-ligand interactions. The project focuses on developing fluorinated probes for monitoring conformational changes in proteins and implementing 19F-detected fragment-based screening to identify weak-binding drug leads. By integrating 19F NMR with structural biology and medicinal chemistry, this research aims to uncover new insights into biomolecular recognition and contribute to early-stage therapeutic development.

Human mucosal surfaces, such as the airway, contain carbohydrate structures (glycans) and many bacteria have evolved carbohydrate-binding proteins that enable infection of host cells. Our aim is to identify glycans that host-adapted bacterial pathogens bind to during colonisation and disease. This project will focus on bacteria including Neisseria gonorrhoeae (causes gonorrhoea) and Neisseria meningitidis (causes sepsis and meningitis). We will probe Glycan Arrays (consisting of >400 sugars immobilised onto glass-slides) using recombinant proteins and wild type bacteria and a series of mutant strains lacking key outer membrane structures. The affinity and kinetics of interactions will be investigated using surface plasmon resonance. We will also use epithelial cell adherence and invasion assays to investigate the functional role of glycan-based host-pathogen interactions. These findings will contribute to understanding key bacterial and host factors involved in colonisation and disease, and may direct development of new drugs and vaccines for these bacteria.

This project investigates how microbial imbalance in the upper airway contributes to glial activation in both the peripheral and central nervous systems, potentially initiating or accelerating neurodegeneration

Stress granules (SGs) are RNA–protein assemblies that regulate cellular stress responses. Persistent or dysfunctional SGs have been implicated in neurodegenerative diseases. This project explores how infections particularly chronic or subclinical bacterial infections disrupt SG dynamics in glial cells from both the peripheral and central nervous systems. Using models of glial exposure to bacterial pathogens or their products, the student will track SG formation, persistence, and clearance using advanced imaging and molecular profiling techniques. The project will investigate whether infection-induced SG dysregulation contributes to chronic inflammation, impaired glial function, and neuronal vulnerability.

The upper airways are a major entry point for many pathogens, including influenza viruses, Streptococus pyogenes (Strep A), and coronaviruses. To combat pathogens that infect upper airways, we aim to develop new lipid, protein, and messenger RNA (mRNA) technologies. We have pioneered a novel lipid delivery system (LDS) that delivers mRNA or proteins efficiently intranasally and is capable of storing mRNA and biological products at room temperature for extended periods of time. By incorporating lipid-linked sugars (glycolipids), secretory immunoglobulin A (IgA)-mediated mucosal immunity is enhanced, which reduces infectivity. This foundation will be built upon for the delivery of vaccines, antibodies and antiviral proteins to the mucosa, providing enhanced protection against influenza A and B viruses as well as Strep A infections. It involves malking delivery systems, testing them in pre-clinical models, and performing immunological and functional tests.

Human mucosal surfaces, such as the airway, are rich in carbohydrate structures (glycans) that many bacteria exploit to infect host cells. This project aims to identify specific glycans targeted by host-adapted pathogens during colonisation and disease. We will focus on pathogenic Neisseria species, using recombinant proteins and both wild-type and mutant strains to investigate glycan binding through techniques including glycan arrays and Surface Plasmon Resonance (SPR). In vitro cell adherence and invasion assays will assess the functional relevance of these interactions. These findings will enhance our understanding of glycan-mediated host-pathogen interactions and support the development of novel therapeutics or vaccines.

The study of carbohydrate structures (glycans) is essential to understanding host-pathogen interactions. Glycans play critical roles throughout infection, from colonisation to disease progression. Neisseria gonorrhoeae (Ng) and Neisseria meningitidis (Nm) are Gram-negative diplococci and major global health threats that exploit host glycans during infection. Our recent studies identified over 200 glycan interactions with these pathogens. This project aims to build on these findings by profiling changes in host-cell glycan expression during infection with Ng and Nm. Using molecular cell biology, microbiology, proteomics, and mass spectrometry, we will investigate how infection alters glycan landscapes and identify potential bacterial receptors. These insights will deepen our understanding of Neisseria pathogenesis and may uncover novel targets for therapeutic or vaccine development.

Understanding the pathobiology of Neisseria spp. requires tools that enable real-time tracking of bacterial behaviour. This project aims to develop genetic tools for the stable expression of fluorescent markers such as GFP, mCherry, mRuby, or mScarlet in Neisseria gonorrhoeae and Neisseria meningitidis. Using molecular microbiology and genetic engineering techniques, we will construct plasmids to generate fluorescent bacterial strains via chromosomal integration. These live, fluorescent Neisseria strains will be validated in an in vivo cell culture model and applied in functional assays requiring bacterial quantification, including cell infection, serum bactericidal, and opsonophagocytic killing assays. The resulting tools will facilitate dynamic studies of Neisseria pathogenesis and host-pathogen interactions.