QUEX PhD Scholarship

Project team

UQ - Dr Jurij Karlovsek

Exeter - Dr Man Luo

Project description

Background and context

The increasing complexity of underground infrastructure, particularly in the rail sector, demands advanced monitoring and predictive maintenance to ensure long-term safety, efficiency, and resilience. Traditional inspection methods and isolated sensor networks often lead to fragmented data, delayed response times, and operational inefficiencies.

This project proposes the development of an integrated framework for tunnel monitoring and durability assessment, utilising Digital Twin technology, real-time sensor data, and Machine Learning (ML). The framework aims to transform tunnel asset management through intelligent, data-driven insights that optimise lifecycle operations.

Aims, Objectives, and Deliverables

This project aims to deliver a scalable and intelligent monitoring framework that enhances tunnel resilience and predictive maintenance through real-time data integration and AI analytics.

Objectives:

• Develop a real-time Digital Twin framework for tunnel structural health monitoring.
• Apply ML algorithms to detect failure mechanisms and predict deterioration trends.
• Ensure scalability for deployment across diverse tunnel networks.
• Validate the framework using operational data from real-world rail tunnel environments.

Key Deliverables:

• A Digital Twin-based system with integrated sensor monitoring capabilities.
• AI/ML-driven models for risk detection and predictive maintenance.
• A replicable methodology for large-scale tunnel infrastructure digitalisation.
• Technical documentation, academic publications, and workshops for industry uptake.

Approach and Methods

This multidisciplinary research integrates geotechnical engineering, AI, digital modelling, and sensor technologies:

• Data Acquisition & Integration: Deploy sensor networks to monitor tunnel deformation, water ingress, and environmental parameters.
• Digital Twin Development: Link sensor data with BIM-based 3D models to enable real-time simulations of tunnel behaviour.
• Machine Learning Application: Develop models to detect anomalies, forecast degradation, and guide maintenance planning.
• Scalability and Adaptation: Design the framework for seamless scaling and integration across regional and national tunnel systems.

Institutional Expertise and Facilities

UQ contributes expertise in geotechnical and digital engineering, with strong experience in applying real-time monitoring systems and lifecycle asset management in the rail sector. The University of Exeter adds world-leading capabilities in computational modelling, structural health monitoring, and AI-driven predictive maintenance.

Together, the project team brings the technical capability, research infrastructure, and industry alignment needed to deliver a transformative approach to tunnel resilience and sustainable rail operations.

Contact

Questions about this project should be directed to Dr Jurij Karlovsek j.karlovsek@uq.edu.au​​​​​

Project team

UQ - Professor Matthew Davis

Exeter - Professor Janet Anders

Project description

Technical details

This project aims to develop protocols demonstrating quantum advantage in thermal machines in ultracold atom systems. Ultracold atom systems allow for fantastic control in experiments: it is possible to manipulate and control trapping potentials, dimensionality, the strength of atomic interactions and even the number of atomic species.  These wide-ranging control parameters allow the realisation of a variety of many-body states of matter exhibiting quantum correlations and coherences. Australian laboratories have an impressive history of ground-breaking research in the field of ultracold atoms. Ultimately, the aim of the project is to develop proposals that will be realised in the experimental laboratories of close University of Queensland collaborators Dr Tyler Neely and Prof. Halina Rubinsztein-Dunlop, who are world-leading in the control and manipulation of ultracold quantum gases.

Project outline

The project will consist of two parts.

1. We propose to utilise the coherence possible in multicomponent ultracold atom systems to devise a cyclic quantum heat engine. This generalizes a conceptually simpler model proposed by Prof. Anders [7] to a realistic multicomponent atomic gas where the protocol can be tested.  A first step has been taken recently by the proposed project supervisors [8]. Such a realisation will, for the first time, demonstrate Anders’ proposal in a many-body system and offer the potential for scaling the effect to a mesoscopic size.
2. Recent theoretical work has demonstrated that quantum entanglement can be used as a thermodynamic resource [9]. Utilising this idea, the PhD student will explore proposals to develop a quantum thermal machine based on ultracold atoms that utilises entanglement to outperform classical engine equivalents. This is particularly relevant to quantum computers, which rely on quantum entanglement to store and process exponentially large amounts of information. Understanding the role and potential usefulness of entanglement in quantum thermal cycles has the potential to transform how we understand quantum computers, circumventing or exploiting the major obstacle of environment interactions. Prof. Anders’ expertise in quantum information – in particular utilising entanglement as an information resource [10] – will be indispensable in this study.

In this project the PhD student will identify an experimentally realisable quantum advantage in heat engine operation using coherence and entanglement. Throughout, we will couch our understanding in an information theoretic framework to provide maximum overlap with quantum computing and information protocols. Such an approach offers the potential to circumvent or even utilise thermodynamic obstacles present in current quantum computing proposals. In addition to the immediate practical value of this project, the understanding gained will help answer fundamental questions regarding the nature of thermalization and heat engine operation at the quantum level. This continues to pose unanswered questions since the inception of the theory almost 200 years ago. This powerful combination of fundamental understanding and practical implementation has the potential to have a major impact on cutting edge quantum digital technology, and the exciting and disruptive impact of this area on the digital and technological world.

[1]       C. Bennet and D. DiVincenzo, Quantum information and computation, Nature 404, 247 (2000).

[2]        J. P. Dowling and G. J. Milburn, Quantum technology: the second quantum revolution, Philos. Trans. R. Soc. Lond. A 361, 1655 (2003).

[3]        V. Vedral, The role of relative entropy in quantum information theory, Rev. Mod. Phys. 74, 197 (2002).

[4]        J. Robnagel et al. Nanoscale heat engine beyond the Carnot limit, Phys. Rev. Lett. 112, 030602 (2014).

[5]        M. Scully et al. Extracting work from a single heat bath via vanishing quantum coherence, Science 299, 862 (2003).

[6]        M. Ueda, Quantum equilibriation, thermalization and prethermalization in ultracold atoms, Nat. Rev. Phys. 2, 669 (2020).

[7]       P. Kammerlander and J. Anders, Coherence and measurement in quantum thermodynamics, Sci. Rep. 6, 22174 (2016).

[8]       L. A. Williamson, F. Cerisola, J. Anders and Matthew J Davis, 2025 Quantum Sci. Technol. 10, 015040 (2025).

[9]        A. Touil, B. Çakmak and S. Deffner, Ergotropy from quantum and classical correlations,    J. Phys. A: Math. Theor. 55, 025301 (2022).

[10]      J. Anders and D. Browne, Computational power of correlations, Phys. Rev. Lett. 102, 050502 (2009).

Contact

Questions about this project should be directed to Professor Matthew Davis mdavis@uq.edu.au​​

Project team

UQ - Associate Professor Sarit Kaserzon

Exeter - Associate Professor Edward Keedwell

Project description

Background

Maintaining water security remains one of the greatest challenges globally. Accordingly, providing clean drinking water is a UN goal for sustainable development (SDG6). Incidents of drinking water contamination have increased over recent years, challenging current water resource management. Growing numbers of water contamination scenarios are reported (e.g. PFAS contamination in drinking water supplies), exacerbated by climatic events (i.e., floods, algal blooms, increased pollution). Concerningly, current monitoring practices involve several costly and disparate analytical techniques and only target a very limited number of regulated contaminants. The rate at which chemicals enter waterways far outpaces current regulatory methodologies. Therefore, strategies that can timely identify environmental and human hazards are paramount for adequate risk management.

Aims/Objectives/Approach/Deliverables

This project aims to place a student at the forefront of innovation and technology by developing the capabilities to enable robust identification of chemical threats in water systems, that is fit-for-purpose and adaptable to changing climate and environmental stressors. Such a tool does not exist, but is required to support water authorities, environmental and health protection regulators and water laws. Starting at UQ year 1, samples will be run using established HRMS methods at QAEHS and used to generate the data to build, train and test the ML models in Exeter (years 2-3). Key deliverables include:

1. Obtain training set data from Australian/UK water treatment plants to establish ‘typical baseline chemical fingerprints’ (ground truth data), including time-series data.
2. Develop train and test ML models based on HRMS water quality fingerprinting.
3. Develop the anomaly detection mechanism using the sampling cycle, HRMS, ML and fine-tuning.
4. Stress-test using different water baseline parameter scenarios (i.e. post floods with extremely turbid water or when water sources are mixed).

Main deliverable will be a highly novel PhD Thesis with several publications highlighting an open access ML tool, ready for validation in large test case applications, starting with water authority collaborators, followed by other facilities. Future commercialisation would also be a possibility.

Expertise/Facilities

QAEHS consistently maintains high research outputs and success in major Australian and international research grants. It operates through a state-of-the-art laboratory with instrumentation equipped for trace-micropollutant analysis (14xGC and LC-MS/MS Incl. 4xHRMSs), with a highly supportive environment for PhD students (~40 PhD’s from 15 countries). Facilities at Exeter include three complementary aspects; the Department of Computer Science (DCS), Centre for Water Systems (CWS) and the Centre for Resilience in Environment, Water and Waste (CREWW). In REF2021, 95% of research outputs in the DCS were rated internationally excellent, 41% as world-leading, with a cohort of ~70 PhD’s. Computationally, Keedwell’s group includes access to 2xhigh-powered workstations and server and the ISCA supercomputing facility.

Both UQ/Exeter groups have established long-term meaningful collaboration with the Australian/UK water industries. E.g. several technologies developed by CI Kaserzon’s team are today applied by industry in Australia and globally (>$9M funding). While CI Keedwell’s team have a proven track record working with the water industry on improving industrial knowledge systems and applying ML optimisation to solve problems in the water sector (>£5M funding; EPSRC, Innovate UK, EU and industry).

Contact

Questions about this project should be directed to Associate Professor Sarit Kaserzon k.sarit@uq.edu.au

Project team

UQ - Professor Peter Mumby

Exeter - Associate Professor Rachel Turner

Project description

UQ-based research has advanced the linking of MPAs to fisheries benefits by developing novel ecological and fisheries models. However, like most fisheries theory, the representation of “fishing pressure” – reduced inside MPAs – is an abstraction of reality that is reliant upon theory (Maximum Sustainable Yield). The final step in connecting MPAs to fisheries benefits is to translate ‘fishing pressure’ into fisheries management measures that restrict fishing activities inside the MPA. This might include restrictions to fishing methods (nets, line, spears), the sizes of fish that can be caught, limits to catch, or bans on harvesting vulnerable species. There is a need to understand how different combinations of restrictions alter fishing pressure. This can then be entered into existing models of the fishery and MPA.

Yet not all fishing restrictions are equal. Fishing and fisheries are subject to social and economic norms and constraints. Some restrictions will be far less socially acceptable than others. Thus, we need to take an interdisciplinary approach at the boundary of natural and social science. This PhD project will link MPA management (practical fisheries restrictions) to their wider fisheries outcomes. That alone is a unique and important contribution to the field. But the real innovation is to add the social opportunities and constraints, which means that realistic trade-offs between alternative MPA options can be quantified.

The supervisory team includes natural and social scientists. Primary advisor is Prof Peter Mumby, an UQ ARC Laureate with a long history of achieving practical conservation outcomes from his research on coral reefs. Peter has graduated 35 PhD students as primary advisor while at UoE and UQ (he moved in 2010). He has led multidisciplinary projects funded by the World Bank and EU, and he met CI Associate Prof Rachel Turner (UoE) on the latter, when she worked as a post-doc at the University of the West Indies. Rachel is an environmental social scientist with expertise in resource use behaviour and marine governance. Rachel will lead the social science on the project. CI Ruth Thurstan is a historical ecologist with extensive expertise in fisheries, how their management has evolved. She is particularly interested in how historical norms of fishing behaviour impact the feasibility of fisheries restrictions.

Undertaking social science on fisher behaviour and values is notoriously challenging in terms of achieving trust and overcoming cultural and language barriers. Yet, this project offers an extraordinary opportunity to do this in a PhD timeframe. Under Mumby’s leadership, UQ will soon be executing a 5-year MPA/fisheries project in the Gulf-of-Thailand for the UN Food & Agricultural Organisation (FAO). The project, named GoTFish, is a partnership with the Malaysian Department of Fisheries (DoF) who are seeking help in meeting their 30x30 commitment. While the PhD student will not be funded by the project directly, they will add value and benefit from logistical support (e.g., local data collectors), long-term partnership between the DoF and community groups, and access to DoF staff, which provides a unique opportunity to understand the management constraints on fisheries restrictions.

Contact

Questions about this project should be directed to Professor Peter Mumby p.j.mumby@uq.edu.au

Project team

UQ - Dr Shelley Keating

Exeter - Dr Xingchen Zhang

Project description

Background & Context

Recently, we highlighted the exciting potential of digital humans for exercise support [PMID:37652667]. This technology is poised to revolutionise healthcare.

This PhD program will leverage novel technology to collaboratively develop and evaluate an innovative digital human exercise coach. The digital human will combine a sophisticated conversational chatbot with an ultra-realistic 3D digital model. The coach will engage end users (clients and practitioners) to support exercise maintenance.

Aims & Objectives

This interdisciplinary PhD proposes to design and evaluate a digital human exercise coach to support self-directed exercise maintenance in people with cardiometabolic disease.

Outputs will include:

1. an interdisciplinary doctoral thesis that intersects clinical exercise care, digital innovation and behavioural science.
2. at least three academic journal articles that will result in knowledge gain with potential for scaled health impact.
3. Production of a digital resource that can be scaled to industry, large funding and other allied health services.

Methods & Approaches

Adopting a codesign methodology (designing with end users), the student will undertake three key project phases:

1. Development and coding: The student will undertake coding and Large Language Model training to generate and train a prototype (prototype 1). The coach will be generated using an open-source (free) state-of-the-art conversational chatbot and 3D creation suite, capable of modelling ultra-realistic digital humans. 
2. User testing and training:  The coach will incorporate evidence-based support strategies, and an advisory group of clinical exercise physiologists and lived experience clients will conduct user testing (prototype 1). Iterative changes will be made until user testing reaches agreed-upon levels of functionality and acceptability (prototype 2).
3. Evaluation: The student will undertake a randomised controlled feasibility trial of prototype 2 involving people with cardiometabolic disease risk factors through The University of Queensland’s exercise clinics. Following evaluation, an industry-ready prototype (prototype 3) will be finalised.

Fit with expertise at Queensland and Exeter

Keating (UQ) is a clinical exercise physiologist and high-impact researcher who leads a multidisciplinary team in exercise intervention for people with cardiometabolic disease, with a dedicated lens on sustainable exercise solutions. Zhang (Exeter) brings expertise in deep learning, robotics and AI as well as experience in ethical AI (pedestrian privacy protection). Van Beurden (Exeter) is an experienced researcher specialising in the development, evaluation, and implementation of complex health interventions, with a particular focus on digital health. Her work applies theory-, evidence-, and person-centred approaches, including Intervention Mapping and the Person-Based Approach, to co-design intervention materials which frequently includes human-computer interfaces, to optimise engagement with the intervention.  Gilson (UQ) is a trailblazer in technology-based exercise interventions. He has collaborated with thousands of end-users to co-design exercise solutions for chronic disease prevention and management and will provide oversight of the program.

Collectively the advisory team have unique and complementary expertise to support all aspects of this cross-cutting PhD program with an established track record of PhD completion and high-impact PhD outputs.

Contact

Questions about this project should be directed to Dr Shelley Keating s.keating@uq.edu.au

Project team

UQ - Dr Sally Mortlock

Exeter - Professor Anna Murray

Project description

Background

Menopausal symptoms affect 70–80% of women, often disrupting daily life. These include hot flushes, sleep disturbances, and mood changes. Various risk factors, such as obesity, have been proposed, but relationships between symptoms remain unclear. Menopausal symptoms are also linked to increased risks of cardiovascular disease and Type 2 diabetes. However, their aetiology remains poorly understood due to confounding factors in observational studies. A genetic basis for some symptoms has been established, and this project will further investigate genetic and lifestyle determinants using genetic and life-course epidemiology.

Aim & Objectives

To identify genetic and lifestyle determinants of menopausal symptoms and their long-term health impacts, providing aetiological insights to improve population health by:

• Identifying lifetime risk factors increasing the likelihood of menopausal symptoms.
• Determining whether different menopausal symptoms share a common aetiology.
• Evaluating whether experiencing menopausal symptoms affects postmenopausal health.

This project aligns with the Healthy Living theme by improving understanding of factors influencing menopausal symptoms. Findings will guide research, clinical guidelines, and public health strategies, contributing to women’s well-being and healthy aging.

Methodology

The project integrates statistical genetics and life-course epidemiology using large-scale genetic and longitudinal studies. The student will review literature to identify relevant risk factors and health consequences and analyse data from the Australian Longitudinal Study on Women’s Health (ALSWH), a study of over 57,000 Australian women, to assess associations between risk factors, menstrual symptoms, and postmenopausal conditions.

The student will access genome-wide genetic data on menopausal symptoms from a University of Exeter study involving over 700,000 women across 12 global cohorts. These datasets, alongside publicly available GWAS data, will be used to investigate aetiological relationships between menopausal symptoms. Multivariate Mendelian Randomisation will examine whether lifestyle factors (e.g., BMI) contribute to menopausal symptoms and whether symptoms (e.g., hot flushes) are causally linked to postmenopausal health issues (e.g., cognitive decline).

Multi-trait, colocalisation, and genetic clustering techniques will be used to explore biological mechanisms and identify shared genetic risk factors. Models will assess interactions between genetic and non-genetic risk factors, validated using UK Biobank and ALSWH data.

Outcomes

This project will generate novel biological and clinical insights into risk factors, menopausal symptoms, and postmenopausal health. Findings will aid in evaluating treatments (e.g., HRT). Insights will support population health, providing evidence for women, health professionals, and researchers. Outputs include peer-reviewed publications, conference presentations, and potential intervention targets, informing research, clinical care, and policy.

Capability

A collaboration between the University of Queensland (UQ) and the University of Exeter, this project leverages expertise and resources from both institutions. UQ’s Australian Women and Girls' Health Research Centre specializes in menopause symptoms, life-course, and genetic epidemiology, with access to UK Biobank and ALSWH datasets. Exeter’s Reproductive Genomics Team focuses on reproductive health genetics and genome-wide genetic data. Both institutions provide research computing, technical support, and secured data access. The student will spend 6–12 months at UQ for initial analysis and 12 months at Exeter for advanced genetic techniques. Collaboration with clinicians will enhance real-world impact and inform national clinical guidelines.

Contact

Questions about this project should be directed to Dr Sally Mortlock s.mortlock@imb.uq.edu.au

Project team

UQ - Professor Neil McIntyre

Exeter - Dr Jawad Fayaz

Project description

"Modelling pit lake water balance and water quality is standard practice in mine planning and impact assessments. These models simulate hydrological and biogeochemical processes and interactions with surrounding surface waters and groundwaters to understand associated impacts. However, data on pit lakes is sparse, such that basic information (e.g., pit lake location) is often unavailable. Additionally, many pit lakes are not in hydrologic equilibrium meaning that long-term monitoring is critical to capturing process changes over time. 30+ years of widespread satellite RS offers a cost-effective and scalable alternative to in situ monitoring.

Aims and objectives:

This PhD project aims to develop RS-driven approaches for automatic detection and monitoring of pit lakes, improving understanding of their hydrological and chemical dynamics. Specific objectives include:

1. Developing deep learning algorithms for detecting, mapping, and tracking pit lake dynamics.
2. Investigating drivers of pit lake hydrology and water quality using a large sample database in Australia
3. Integrating RS-derived data into predictive models to demonstrate RS utility in pit lake modelling and management

Key deliverables:

1. Detailed literature evaluation of conventional water detection indices (e.g., NDWI) for pit lakes.
2. Novel deep learning algorithms tailored for pit lake monitoring, including:
   1. Multi-sensor fusion model to map water extents and volumes across diverse pit lake types.
   2. Open-source code repositories and validation benchmarks for reproducibility.
3. A national pit lake database for Australia.
4. Case studies demonstrating RS applications in pit lake numerical modelling and management.

Methods:

1. Pit lake algorithm development –Fully convolutional neural network (U-net) will be designed to automatically detect and map pit lake water extents using freely available satellite imagery (Landsat, Sentinel-1/2). This will be combined with altimetry data (SWOT) to estimate water level–area–storage relationships. Temporal trends will be modelled using transformers to capture seasonal and long-term changes. Algorithm performance will be validated using case study sites with in situ monitoring data.
2. Investigating pit lake dynamics – A large-scale pit lake database – including pit locations, dynamics, water colour - will be constructed using RS-derived observations. Explainable AI techniques, such as Shapley values, will be applied to assess key drivers of hydrological and biogeochemical variability, considering climate, groundwater connectivity, and pit morphology. This analysis will be supported by external datasets from sources such as NASA, BOM.
3. RS integration in predictive modelling – Example application includes: Historical RS-derived water levels will be used for calibrating and validating lake hydrodynamic models, improving predictions of water stratification and quality evolution.

Institutional expertise:

UQ’s Sustainable Minerals Institute (SMI) offers world-leading expertise in water management and mining-related RS applications ensures access to case study sites and real-world validation datasets (e.g., through the CRC-TiME pit lakes project). UQ’s partnership with the National Computing Infrastructure provides access to extensive satellite imagery archives. The University of Exeter’s Department of Computer Science brings expertise in AI-driven temporal modelling (Dr. Fayaz) and computer vision (Dr. Rowlands). Access to the ISCA HPC cluster, Camborne School of Mines’ industry networks, and the Environmental Intelligence network supports scalable AI solutions for pit lake monitoring."

Contact

Questions about this project should be directed to Professor Neil McIntyre n.mcintyre@uq.edu.au

Project team

UQ - Professor Daniel Franks

Exeter - Dr Deborah Johnson

Project description

This project will explore for the first time the geographic distribution of mineral insecurity, through an investigation of the accessibility and affordability of mineral fertilisers and cements.

Africa produces 30 million tons of fertilizer annually but exports most of it and accounts for just 4% of global consumption – a consequence of high costs and complex supply chains. Local crushed rocks offer a promising alternative source of crop nutrients, with the Rochagem movement in Brazil pioneering the use of local rocks, cutting costs by up to 80% while producing crop yields equal to or higher than those obtained with conventional fertilizers.

Similarly, Sub-Saharan Africa consumes only 3% of global cement production despite comprising 18% of the world’s population. The high cost of imported clinker cement impedes economic development, weakens housing and transportation infrastructure, slows recovery efforts following natural disasters, and limits African countries’ ability to protect coastlines from the effects of climate change. Alternatives like limestone calcined clay cement (LC3) can be produced locally from abundant clay resources with up to 25% lower costs and 40% lower carbon emissions.

Aims and objectives

• Develop a pioneering method to represent the geographical  distribution of mineral insecurity.
• Begin building an evidence base to support inclusion of mineral security in measures of human development and poverty.
• Use this evidence to influence policy agendas focused on poverty, including inclusion of minerals in the next SDGs.

The research project will analyse the geographic distribution of fertiliser and cement accessibility in Africa by analysing prices, availability and affordability. The candidate will develop or adapt a database of prices and compare to purchasing power parity investigating both local and imported supply chains. This will involve collection of data on the prices of mineral fertilizers and cement at the various scales (both imported and domestically produced), determination of their affordability for rural and urban households (considering factors such as income) and development of a mineral security index that would be used to create GIS-based maps to enable comparisons between different geographies. These maps can subsequently be overlaid with similar maps created using multi-dimensional poverty to explore the confluence between mineral insecurity and other poverty dimensions.

The candidate will be hosted at the Global Centre for Mineral Security at The University of Queensland’s (UQ) Sustainable Minerals Institute (SMI) and the Department of Politics and International Relations at The University of Exeter.

The Global Centre for Mineral Security is a group of social scientists, economists, geographers, ecologists, geologists, and engineers with hands-on development experience, dedicated to reducing poverty, improving livelihoods, and enhancing sustainable development. The Centre works with a wide range of development partners, including UNDP and The World Bank. The candidate will be associated with an Australian Research Council Future Fellowship on ‘Assessing the mineral security dimensions of multi-dimensional poverty’.

The Department of Humanities and Social Sciences Cornwall brings together specialisms in Politics and International Relations, (Environmental) Humanities and Law to examines societies, cultures and justice in the past, present and future.  The department contributes to leadership of the international People and Mining network, has well established teaching and research links with the Camborne School of Mines and works closely with mining focused civil society actors."

Contact

Questions about this project should be directed to Professor Daniel Franks d.franks@uq.edu.au

Project team

Exeter – Dr Xu Chu

UQ - Associate Professor Rowan Gollan

Project description

Background and Context

Scramjet engines are key enablers for hypersonic flight, promising efficient propulsion at speeds of Mach 5 and beyond. Yet the risk of unstart—where internal shock structures propagate upstream, choking the flow—threatens performance and stability. Prior studies have shown that unstart can be highly sensitive to small variations in pressure ratio and inlet boundary-layer profiles. Despite decades of investigation, many predictive tools lack robust ways to incorporate uncertainties in boundary conditions, turbulence modeling, and manufacturing variability.

Problem Statement

Conventional CFD workflows assume deterministic inputs, often using “best guess” values. These methods miss the probabilistic nature of input parameters, thereby underestimating unstart risks and limiting confidence in scramjet operability margins. Recent efforts integrated dimensionally adaptive sparse-grid techniques with RANS-based CFD, demonstrating that different probability distributions of uncertain parameters can drastically alter the predicted unstart risk. However, the published framework remains at a proof-of-concept stage, and broader application to realistic flight conditions requires further refinement.

Aims and Objectives

Refine the UQ-CFD Framework: Extend the existing adaptive sparse-grid approach to handle higher-dimensional sets of uncertain parameters (e.g., heat-release effects, geometric variations).

Quantify Unstart Risks: Calculate probabilities of leading-shock movement for a range of operating points, capturing the interplay of various uncertainties.

Develop Best-Practice Guidelines: Formulate recommendations for scramjet designers to manage uncertainty in test planning, data analysis, and risk mitigation.

Validate against Experimental Data: Cross-check predictions with relevant experimental setups, such as nozzle-isolator models at NASA facilities.

Methods and Approaches

We will employ the open source JAX-Fluids solver to simulate flow within a representative isolator. This solver will be coupled with an uncertainty quantification toolkit based on adaptive sparse-grid sampling. Parameter distributions (e.g., pressure ratio, boundary-layer shape factor, turbulence modeling coefficients) will be derived from literature, experimental measurements, or expert elicitation. Each simulation’s outcomes—shock location, Mach-stem height, separation zones—will feed back into the UQ module to refine sampling in regions of high sensitivity.

Institutional Expertise and Contributions

The two institutions and research groups provide both complementary and supporting expertise, as follows;

At the UoE; Dr. Chu is an expert in Machine Learning, supersonic flow simulation and the JAX-Fluids code, with strong links to the original authors of this code. Prof Tabor will contribute expertise in turbulence modelling and uncertainty quantification. Initial work at Exeter (0-18 months) will involve developing an appropriate hypersonic model to validate against literature data (experimental data available from existing collaborations with eg. NASA).

At UQueensland, Dr Gollan is an expert in hypersonic flow simulation at the Centre for Hypersonics, and will also contribute expertise in UQ. Dr Gibbons has an world-class background in the simulation of combusting scramjet flows that will contribute the advisory team’s expertise. More broadly, The Centre for Hypersonics can also contribute experimental expertise and knowledge of scramjet physics developed over many years. HypersoniX will also provide an industrial angle to the project particularly through the 1 month placement at the company. Work in Australia (18-30 months) will focus on developing the UQ module and extending the modelling to cover real world scramjet geometries.

The final period (30-42 months, Exeter) will focus on extending the modelling further, publication, and thesis preparation. Deliverables include a validated computational workflow, open-source UQ scripts, documented best-practice guidelines, and at least two peer-reviewed publications demonstrating the approach on relevant scramjet configurations.

Contact

Questions about this project should be directed to Dr Xu Chu X.Chu@exeter.ac.uk
Apply here: Award details | Funding and scholarships for students | University of Exeter

Project team

Exeter – Associate Professor Muhammad Khurram Bhatti

UQ - Dr. Priyanka Singh

Project description

Computing systems today face increasing security threats, including zero-day vulnerabilities, side-channel attacks, and firmware exploits across multiple layers. Traditional methods like signature-based detection and manual penetration testing struggle to keep up with evolving cyber threats targeting microarchitecture and system-level vulnerabilities. The attack surface expands rapidly with new software, hardware, and AI-integrated vulnerabilities, introducing novel attack vectors. For instance, an untrusted OS attacking protected software (Software-on-software attack), an untrusted software using cache side-channels to extract secrets (Software-on-hardware attack), a rogue memory controller exploiting DRAM via Rowhammer (Hardware-on-software attack), and a malicious peripheral disabling memory encryption (Hardware-on-hardware attack). Integration of AI introduces additional vulnerabilities across security domains, affecting malware detection and vulnerability discovery. Conventional mitigation techniques focus on specific vulnerabilities rather than a system-wide approach, often sacrificing performance.

This research aims to balance security and performance by advocating for AI-driven runtime detection-based mitigation. The candidate will design techniques for automated assessment of attack surfaces and vulnerabilities across software/hardware layers.

This project has multiple interdisciplinary objectives and deliverables to achieve as mentioned in the following:

Deliverable 1 (Interdisciplinary): Led by Prof Ryan (UQ) and Dr Ruchi (UoE), with inputs from the industrial partner, this deliverable will focus on the assessment of potential economic impact caused by the non-compliance of security standards and the use of insecure hardware/software by the industry. Results will be shared with local industry, research networks, and government bodies.

Deliverable 2 (disciplinary): Candidate will design and develop AI-enabled models for automated assessment of attack surface and vulnerabilities across software/hardware abstraction layers that are caused by the intrinsic system design, performance enhancements, and integration of AI. These results will be published in peer-reviewed conferences/journals. Prof. Khurram (UoE) and Prof. Achim (UoE) will lead this research from the hardware perspective, whereas Dr Priyanka (UQ) will advise on the system software and AI security vulnerabilities.

Deliverable 3 (disciplinary): Candidate will leverage the results from Deliverable 2 to develop run-time detection-based mitigation solutions at the system software level for enhanced security using AI models. These novel techniques will provide protection against a large attack vector and improve security guarantees of computing systems. With the help of industrial partner, the candidate will develop use-case scenarios for the implementation.

Deliverable 4 (Interdisciplinary): This deliverable will focus on the analysis of business potential, exploitation and commercialisation of scientific results being produced under this PhD proposal.

Deliverable 5 (Interdisciplinary): A key deliverable is capacity building for the candidate and fostering a long-term research network. The candidate will receive career development and training through institutions and industry collaboration. Research findings will be shared with clusters like BSides Exeter, South-West Cyber Security Cluster (UK), and CyberCERT (AUS). This deliverable is led by Prof. Achim (UoE) and Prof. Ryan (UQ).

Timeline:

Candidate will spend 30 months at UoE (0-18 months and 31-42 months, respectively) and will complete Deliverables 1, 2, and 5 (partially). For Deliverables 3, 4 and 5 (partially), candidate will visit UQ for 12 months (19-30 months). 

Contact

Questions about this project should be directed to Associate Professor Muhammad Khurram Bhatti K.Bhatti@exeter.ac.uk
Apply here: Award details | Funding and scholarships for students | University of Exeter

Project team

Exeter – Professor Will Gaze

UQ - Dr Jake O'Brien

Project description

Antimicrobial resistance (AMR) occurs when microorganisms, such as bacteria, fungi, parasites, and viruses, adapt to current treatments like antibiotics, reducing their effectiveness. This can happen through genetic mutations or the transfer of resistance genes between species (many of which originate in environmental bacteria). Human use of antimicrobials, including overuse and misuse, drives the development of multi-drug resistance, becoming a rapidly growing global health problem. The World Health Organization (WHO) launched the GLobal Antimicrobial resistance Surveillance System (GLASS) in 2015 to monitor this issue, but critical data gaps remain in monitoring significant pathogens. Current data suggests that currently between 1-5 million people die annually from AMR infections.

AMR is not limited to clinical settings nor to humans, as resistance genes can be mobilised between bacteria in various environments. Animal waste acts as a major environmental reservoir for AMR due to the presence of excreted faecal matter, urine, faecal bacteria, and antimicrobial drug residues. Unlike humans who have dedicated systems to treat and remove contaminants from waste prior to release, waste generated in animal husbandry are either directly released to the environment or may be reused for other agricultural purposes. As such there is risk associated with AMR and antibiotic-resistant bacteria (ARB). As Zero Hunger is the #2 Sustainable Development Goal, this currently unquantified risk to food production requires investigation.

This PhD project thus aims to 1) derive agricultural specific selective effect concentrations of antimicrobials and determine the risk they pose in terms of AMR evolution, 2) characterise AMR bacterial populations associated with agricultural waste streams and to assess their dissemination to the wider environment including aquatic systems used for irrigation, water abstraction and recreation and 3) using mesocosms experiments determine the ability of human opportunistic pathogens such as E. coli to acquire novel resistance mechanisms during passage through the environment. Recent work in coastal environments has revealed that some E. coli isolates are much better adapted to survive or even grown in the environment and only by focusing on a range of strains, including these environmentally adapted strains, can we understand the full risk posed by in situ evolution of human pathogens in environmental compartments.

The project will combine microbiological and chemical analyses to characterise agricultural samples followed by adapting the SELection End points in Communities of bacTeria (SELECT) method to derive agriculture specific selective effect concentrations of antimicrobials. This will enable risk assessment to be conducted applicable for both agriculture and the environment.

The European Centre for Environment and Human Health at Exeter are world leaders in environmental AMR surveillance and Professor Gaze leads a transnational group of academics and government practitioners via UKRI AMR network. The Queensland Alliance for Environmental Health Sciences (QAEHS) at The University of Queensland has pioneered environmental sampling and chemical analysis for antimicrobials since 2019. Their facilities include state-of-the-art trace analytical chemistry infrastructure and the Australian Environmental Specimen Bank (ESB) which includes agricultural samples. As such the capabilities of each institution are complimentary and sampling and chemical analysis will be conducted at UQ and microbial characterisation at Exeter.

Contact

Questions about this project should be directed to Professor Will Gaze w.h.gaze@exeter.ac.uk
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Project team

Exeter – Dr Jawad Fayaz

UQ - Professor Steven Kenway

Project description

Urban sewer and stormwater systems face escalating failures due to climate extremes and urbanisation. Ageing infrastructure, designed for historical rainfall patterns, now struggles with frequent “1-in-100-year” storms and urban sprawl, which increase toxic overflows, breach environmental regulations, and disproportionately harm marginalised communities. Traditional models like SWMM are computationally slow and lack scalability, while opaque AI methods risk biased outcomes. This project addresses these gaps by developing a responsible machine-learning framework that integrates climate resilience, equity, and cost-effectiveness into infrastructure management, aligning with UN SDGs 6 (clean-water) and 11 (sustainable-cities).

Objectives

• Develop physics-based ML models to simulate sewer networks as dynamic systems, targeting ≥90% modelling accuracy.
• Train an explainable decision-making agent to optimize interventions (e.g., pipe upgrades), balancing cost, equity, and compliance.
• Resolve data harmonization challenges across utility systems to ensure tool functionality.
• Validate outcomes through case studies in Brisbane and Exeter, targeting ≥20% overflow reduction.
• Deliver open-source tools and training modules for global utility adoption.

The framework combines physics-informed graph-neural-networks (GNNs), diffusion model, and explainable reinforcement learning (XRL) to simulate sewer/stormwater system behaviour, predict risks, and optimize interventions. GNNs act as surrogate digital twins, embedding hydraulic principles to model how land-use changes and extreme weather impact flows. Nodes (junctions, tanks) and edges (pipes) encode hydraulic and climate data, predicting vulnerabilities like overflows.

A generative AI diffusion model synthesizes high-resolution climate-urban scenarios by downscaling global climate maps (e.g., CMIP6) and integrating urban growth projections. Combined with Bayesian uncertainty analysis, the simulated scenarios are used alongwith GNNs to identify overflow hotspots and pressure deficits.

A multi-objective explainable reinforcement learning (XRL) engine then optimizes interventions against environmental, financial, regulatory, and equity goals. Explainability tools—saliency maps, counterfactual analyses, and GNNExplainer—quantify trade-offs and clarify how actions reduce risks, building trust, ensuring regulatory compliance, and minimizing service disparities. Auditable decision trails mitigate bias.

DevOps/API pipelines automate deployment, while interactive dashboards visualize risks, policy impacts, and intervention outcomes. This end-to-end approach balances technical precision with transparency, enabling utilities to preempt failures and prioritize equitable, low-carbon solutions.

Institutional Expertise:

• Exeter: Dr. Fayaz (physics-informed ML) and Prof. Javadi (hydraulic modelling) advance AI development using IDSAI’s GPU clusters, ISCA HPC, and Southwest Water’s utility datasets. Collaboration with HRWallingford enhances industry adoption and testing.
• UQ: Prof. Kenway (urban water systems) and Dr. Moravej (water networks) provide SWMM integration and field validation via ACWEB, utilizing Urban Utilities data. Dr. Gibbes (hydroinformatics) guides scenario generation and policy formulations.
• Collaboration: Joint workshops align tools with utility needs. Exeter develops the ML architecture; UQ validates models and deploys case studies.

Collaboration Phases:

• Months 1–18 (Exeter): Data collation, GNN/diffusion model development.
• Months 18–30 (UQ): XRL policy optimisation and historical validation.
• Months 30–36 (Both): Case study deployment in Brisbane/Exeter, open-source release.
• Months 36–42 (Exeter): Thesis completion, digital twin deployment.

Deliverables:

• Python code for a modular framework integrating physics-informed GNNs, diffusion model, and XRL agent, with DevOps/APIs for utility integration.
• Report analysing overflow reduction, cost savings, and equity improvements.
• Dashboard visualizing overflow hotspots and intervention impact.
• >2 peer-reviewed publications (e.g., Water Research) and training modules.

Contact

Questions about this project should be directed to Dr Jawad Fayaz J.Fayaz@exeter.ac.uk
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Project team

Exeter – Dr Rupam Das

UQ - Associate Professor Susannah Tye

Project description

Background and context:

Optogenetics is a powerful and controlled neuromodulation technique, which mostly used to study the brain by using neural implant containing light to stimulate genetically modified neurons. Traditional brain implants are made of metals like platinum and iridium, which severely limit miniaturisation and signal resolution and, as a result, cause major adverse effects1,2. Furthermore, optogenetics methods for powering the neural implants relies on stiff and tethered (e.g. optical fibres) systems. Due to the remarkable qualities of graphene, including its light weight, biocompatibility, flexibility, and exceptional conductivity, can be used to create considerably smaller devices that are safer to implant and that can be wirelessly powered.

Aims and objectives:

This project aims to model, fabricate, and characterise a tiny, biointegrated and scalable neural implant for optogenetic modulation by combining wireless functionality.

The objectives and deliverables of the project are a) a device model containing the implant and an implantable antenna4 for wireless operation (Exeter), b) fabrication of the implant using graphene (Exeter), c) Development of scalable, programmable and controlled optogenetics(Glasgow), d) Characterisation and experimental validation (Queensland).

Design, method, and plan:

To achieve the objectives, a 42-month (m) PhD project will be carried out through four work packages (WPs).

WP 1. Device modelling of the implant (m1- m10): This objective will focus on modelling of the neural implant. For implant modelling, several simulation softwares (HFSS, SPICE, COMSOL and Sim4Life) will be utilised to characterise the implant.

WP 2. Implant Fabrication (m8 – m22): This objective will focus on fabrication of the neural implant. To replace traditional conductive materials (e.g. Au, Pt) used in neural implant, graphene as a flexible and transparent conductor will be used. Fabrication protocol will be developed, and Exeter’s graphene centre will be used.

WP 3. Designing a versatile implant and internal readout circuit (m23 – m28): The project introduces innovative wireless probes with blue, green, or red µLEDs on a single shank, allowing scalable implants compatible with advanced optogenetic tools. The electronic readout circuit to include multiple µLEDs will be developed at the University of Glasgow’s James watt nanofabrication centre with in-kind support from Prof Hadi.

WP 4. Implant characterisation and validation (m29 – m40): The prototypes developed during WP 3 will be experimentally validated in terms of toxicity, wireless power capability and the potential to modulate behaviour.  This will include validation of dopamine release in response to optogenetic using electrochemical recording methods such as fast scan cyclic voltammetry in development for closed-loop neuromodulation systems. Access to exceptional technical resources and facilities at Queensland Brain Institute (QBI), will support the success and translation of this research, with fully equipped surgical operating rooms, behavioural labs, histology, microscopy facilities, and high-performance computing capacity.

WP 4. Thesis writing and reporting (m37 – m42): During this phase, the PhD student will begin writing the thesis, incorporating the project's findings while also participating in activities outlined in WP 4. Guidance and supervision through regular meetings from both supervisors will be provided throughout to ensure successful project completion.

Contact

Questions about this project should be directed to Dr Rupam Das R.K.Das@exeter.ac.uk
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Project team

Exeter – Professor Benjamin Toby Wall

UQ - Professor Benjamin Hankamer

Project description

The challenge of how to sustainably feed a global population anticipated to move close to 10billion in coming decades centres around whether sufficient protein and micronutrient dense food can be produced whilst staying within planetary boundaries (e.g. greenhouse gas emissions). Aside from plant-based foods, various other alternatives to meat and dairy production have been suggested, but each has significant barriers limiting widespread commercialisation and, thus, meaningful environmental impact (van der Heijden et al. 2023a; Williamson et al. 2024a). We have recently reported proof-of-concept data to show algae can be consumed by humans in large enough boluses to provide meal-like amounts of protein and various micronutrients (van der Heijden et al. 2023b; van der Heijden et al. 2024; Williamson et al. 2024b). Further, we showed that this protein can be metabolised and utilised to synthesise skeletal muscle proteins; however, depending on species it showed low bioavailability and palatability.

The aims of the present work are (1) to utilise our collaboration with an innovative industry partner who have created a palatable novel algal source fit for human nutrition (‘white chlorella’; envisaged as a dairy alternative). We will perform human investigations to establish whether embedding this algal source within a habitual diet (e.g. daily ingestion as opposed to a single meal) is able to support healthy metabolism (e.g. muscle protein synthesis) and markers of health (e.g. insulin sensitivity, micronutrient status, microbiome, blood lipids etc.). This will be achieved by applying gold standard methods with human nutritional physiology, such as stable isotope approaches, muscle biopsy and blood sampling techniques, physical function analysis and the quantification of energy expenditure and cardio-metabolic health. This work will deliver a clear answer within an in vivo human trial setting as to whether exchanging meat and dairy in the diet for novel algal products affects robust markers of human health. (2) The work will then move on to take a biosciences approach, leveraging learnings from our previous work as well as that of our industry partner, to create novel algal formulations. Advanced isolation and protein purification steps, genetic manipulations and large scale algal screenings will be applied at Queensland, in work aimed ultimately to deliver a highly concentrated algal protein, rich in micronutrients and palatable, that can then move into this pipeline of human testing.

The work will harness the in vivo human nutritional physiological expertise and cutting edge facilities for human metabolic testing at the University of Exeter, the leading algal Biosciences brought by the centre for solar technology at University of Queenland as well as the innovaton of the commercial partner Algenuity who will provide product and consultation.

References: van der Heijden et al. 2023a J Nutr; van der Heijden et al. 2023b Nut Rev; van der Heijden et al. 2024 B J Nutr; Williamson et al 2024a Trends Plant Sci; Williamson et al. 2024b Front Nutr.

Contact

Questions about this project should be directed to Professor Benjamin Toby Wall b.t.wall@exeter.ac.uk
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Project team

Exeter - Dr Zhenyu Zhang

UQ - Dr Haijiao Lu

Project description

Electrochemical lithium extraction offers an appealing method for extracting lithium from low-concentration brine lake water or seawater, without the need for the time-consuming evaporation process. The basic principle of this system involves two main approaches: (1) driving lithium ions with an external electric current either into an electrode or (2) through a lithium-selective membrane. For example, TiO2-coated LiFePO4 electrode material combined with a pulse electrochemical method was used to adsorb lithium ions from seawater (Joule 2020, 4, 1459.). However, the adsorption suffers from slow kinetics and material degradation by competing ions. The alternative method employs a lithium-ion selective membrane, allowing only lithium ions to be electrically mobilized across it, while other cations are blocked due to crystal mismatch, remaining in the original electrolyte. Typically, the membrane consists of a lithium ion-conducting material, similar to those used as solid electrolytes in all-solid-state lithium-ion batteries. For example, NASICON-type (Li1+xAlyGe2-y(PO4)3, LAGP) (Joule 2018, 2, 1648.) and glass-type (Li0.33La0.56TiO3, LLTO) (Energy Environ. Sci. 2021, 14, 3152.) lithium-ion conductors have been used in this system. As shown in the figure, by continuously introducing seawater or brine water on the anode side, lithium-ion concentration can be increased on the cathode side. Lithium salt precipitation in the cathode side aqueous electrolyte or lithium metal deposition on cathode with organic electrolyte can be achieved. Simultaneously, byproduct such as Cl2/O2 and H2 gases are generated on the electrodes. Although this design has been demonstrated in several publications, significant challenges persist, such as the low selectivity ratio of lithium ions, slow kinetics for the lithium transport, low lithium generation rate and low energy efficiency.

In this project, the main objectives include:

1. Develop novel lithium selective membrane, based on the advanced solid state electrolyte materials for all-solid-state lithium-ion battery. Using surface coating materials to protect and improve the ion selectivity, conductivity, and long-term stability in different electrolyte.
2. Design new electrolyte systems for the electrochemical process, such as catalyst materials on the anode, using of anion exchange membrane for the anode protection electrolyte, and electrolyte of cathode side, to optimize the efficiency of lithium production.
3. Integrate the system with renewable energy sources to achieve sustainable lithium extraction. Demonstrate the practical application of the according to the industry needs.

From the Exeter side, Dr Zhenyu Zhang is an expert in the material degradation study of electrochemical systems, especially in lithium-ion battery and solid-state electrolytes; Prof. Xiaohong Li's team provides specialised knowledge in energy conversion systems such as hydrogen production from sea water and membrane-free flow batteries. From the Queensland side, Prof. Lianzhou Wang and Dr. Haijiao Lu offer expertise in materials synthesis and characterisation and developing catalysts electrochemical reactions. The objectives 1 are expected to be progressed at Queensland with their abundant resources for catalyst synthesis and performance evaluation, and the objectives 2-3 will be achieved at Exeter with the excellent facilities and infrastructures for system development and large-scale demonstration. The collaboration between the teams will leverage the combined knowledge and facilities to ELITE.

Contact

Questions about this project should be directed to Dr Zhenyu Zhang zhenyu.zhang@exeter.ac.uk
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Project team

Exeter – Professor Patrick Foster

UQ - Professor Maureen Hassall

Project description

Mining is a sector that is particularly vulnerable to climate change (Rüttinger, et al. 2016) which affects the whole mining value chain from exploration, to extraction, transport and closure (Pearce et al., 2011). Changing climatic conditions will have both direct (operational and performance-based) and indirect (securing of supplies and rising energy costs) impacts on the mining sector (Sharma et al. 2013). These include water-related impacts (droughts, floods, extreme rainfall), heat-related/temperature impacts (heat strokes) and as well changes in atmospheric pressure, all of which either have, or have the potential to effect mining operations in Australia. Additionally the increase in climate-related hazards mentioned above affects the viability of mining operations and potentially increases operating, transportation, and decommissioning costs (Odell et al., 2018).

To date there is a growing body of research work studying the impacts of climate change and extreme weather events on mining operations and some of this has been undertaken by QUEX partners (eg the EU funded TEXMIN (The impact of EXtreme weather events on MINing operations) by CSM and work by NCCARF with researchers from SMI).

However, little has been undertaken into the specific OSH risks faced by mineworkers. A recent report by the ILO (ILO 2024) states that “OSH protections have struggled to keep up with the evolving risks from climate change, resulting in worker mortality and morbidity” and there is strong evidence that numerous health conditions in workers have been linked to climate change, including cancer, cardiovascular disease, respiratory illnesses, kidney dysfunction and mental health conditions, among many others.

The aims and objectives of this proposed PhD project are:

• To assess the impact of climate change on OHS risks within the Australian mining industry.
• To develop actionable strategies for mitigating identified risks and enhancing worker safety and well-being.

The proposed approach is:

1. Literature Review: Conduct an extensive review of existing research on climate change impacts on mining OHS, focusing on heat stress, extreme weather events, water issues, and mental health.
   Data Analysis: Utilise climate models and mining data to project future OHS risks under various climate scenarios.
2. Field Surveys: Engage with mining personnel through surveys and interviews to gather firsthand insights on perceived risks and any mitigation practices.
3. Risk Modelling: Develop models to predict the likelihood and severity of identified risks under different climate projections.
4. Strategy Workshops: Formulate evidence-based recommendations to address identified OHS risks and organise workshops with industry experts/stakeholders to co-develop practical mitigation and adaptation strategies.

Camborne School of Mines and the Minerals Industry Safety and Health Centre have been delivering leading edge research, education and consulting services to the global mining industry for many  years particularly in the area of OHS risk management from small-scale to large scale mining operations. Similarly both the wider Institutions have extensive research experience in climate change and Exeter works in very close partnership with the UK Met Office and a number of academic staff there have contributed to recent IPCC Reports. This extensive experience and expertise will be leveraged to assist the PhD candidate in identifying and assessing OHS risks as well as modelling future climate scenarios within mining regions. The latter will be undertaken within the UK, whilst minesite studies and workshops will be undertaken in Australia.

References

ILO (2024) Ensuring safety and health at work in a changing climate, Geneva:ILO

Odell S.D., 2018. Mining and climate change: A review and framework for analysis. “The Extractive Industries and Society”, vol. 5, (1)

Rüttinger, L.& Sharma, V., 2016. Climate change and mining. A Foreign Policy Perspective, Adelphi research

Sharma V., van de Graaff S., Loechel B., Franks DM., 2013. Extractive resource development in a changing climate: learning the lessons from extreme weather events in Queensland, Australia, National Climate Change Adaptation Research Facility

Pearce T et al., 2011. Climate Change & Mining in Canada, Mitigations Adaptation Strategies for Global Changes”  vol. 16 (3)

Contact

Questions about this project should be directed to Professor Patrick Foster P.J.Foster@exeter.ac.uk
Apply here: Award details | Funding and scholarships for students | University of Exeter