Supervisors:

Sam Vine, Professor of Psychology - University of Exeter

Guy Wallis, Professor/Director of Research - University of Queensland

Additional Supervisors:

Dr David Harris - Public Health & Sport Science, University of Exeter

Professor Mark Wilson - Public Health & Sport Science, University of Exeter

Project Description:

The increasing influence of digital worlds on the way we train and learn provides exciting possibilities for training and education that reaches further, is more equitable, and provides greater flexibility. Virtual and augmented reality (collectively ‘XR’) have already had a disruptive influence on the field of human skills training. For many industries, including medicine, defence, and aviation, XR now offers possibilities for training from any location, on equipment that does not yet exist, and to experience new and high risk environments. However, we are at a critical juncture where initial excitement around XR is being met with a realisation about the significant gap in our understanding of how XR impacts our brains and perceptual systems and therefore how we learn and behave in digital worlds.

A particular consideration arises from the fact that XR is providing our brains with sensory information that is fundamentally different to the real-world. This disparity could have serious implications for use cases that depend on the development of precise perception-action couplings, such as training. VR and AR create a fully interactive virtual world for users through replacing the sensory (visual, auditory, and haptic) stimuli of the real-world with computer-generated inputs. Compelling VR experiences depend upon misleading our sensory systems to create illusory perceptions of motion or three-dimensional space. But critically, it is unknown how this illusory sensory information will impact on 1) learning in VR, and 2) how learning will transfer out of VR when new sensory information becomes available again.

These missing, altered, or uncertain sensory inputs in VR pose important research questions. Firstly, there are foundational scientific questions to be answered about how XR affects our perceptual system. Secondly, there are more applied questions about how XR can be used by industries for training and a need for these applications to be informed by our understanding of perception in XR.

The project team across UoE and UQ are already working with a range of industrial partners who are motivated to find answers to these questions. This PhD project will draw on the collective expertise in the team to address questions about the digital transformation of training. The work will be specifically applied to the field of aviation where there is a recent push towards XR techniques used as a replacement for traditional flight simulation.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

Raziyeh Farmani, Professor of Water Engineering - University of Exeter

Amin Abbosh, Professor of Electrical Engineering - University of Queensland

Additional Supervisors:

Professor Akbar Javadi, Professor of Geotechnical Engineering, University of Exeter

Project Description

Aging water infrastructure, population growth, and climate and demographic changes are putting great emphasis on better water asset management. Management of buried water assets is a complex problem with significant technological challenges due to vast quantity of buried assets.  This is not only a big challenge in UK (47,406 pipe bursts were reported in England and Wales in 2019-2020) and Australia (25 water main breaks per 100km in 2017-18), it also is a global issue. In addition, 3 and 45 billion litres of water is lost from water systems every day in UK and globally respectively, and real water losses in Australia was 100 litre/connection/day in 2017-18. The World Bank estimates the cost to utilities of water lost at approximately US$14 billion per annum. These put great pressure on water companies to drive down their level of leakage and reduce interruptions to their customers. Water infrastructure spending is expected to total around US$23 trillion globally from 2005 through 2030.

Traditionally, historical failure data analysis methods predict the probability of pipe failure which could be applied across the entire network by grouping assets into cohorts with similar properties (e.g. age, material etc.). In these methods, models are developed and used to capture relationships between pipe properties, their surrounding environment and failure without considering asset condition. Recently, predictive models based on real-time performance monitoring data (e.g. pressure, flow) are used to predict anomalies and isolate failure in a timely manner. These methods are reactive asset management methods and their main aim is to minimise the consequence of failure rather than avoiding failure.

As water companies strive to be more cost-effective by moving from reactive to more proactive asset maintenance and management, a gap is being observed in information about the automated use of information from asset condition assessment. Water companies are adopting some of the latest technologies and innovations, such as ultrasonic, fibre-optic, electromagnetic etc. to drive down the level of leakage in their systems. None of these technologies has been considered for asset condition and actual deterioration monitoring. Electromagnetic waves can be generated and modulated into various polarizations to support multimode operation, which is extremely useful to detect complex, hidden cracks and other types of defects effectively.

This project aims to develop a non-destructive testing and evaluation (NDT&E) technique dedicated to automated and accurate inspection of water infrastructure.  This will allow the prediction of deterioration and the remaining life of buried water assets from monitoring data, therefore establishing the most appropriate and cost-effective intervention options and timing for each asset in the network prior to failure.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

Dr Kimberley Hockings, Senior Lecturer in Conservation Science, Unitversity of Exeter

Dr Matthew Holden, Lecturer in Mathematics, University of Queensland

Project Description:

Artificial Intelligence (AI) is surpassing performance benchmarks and transforming the world as we know it. Yet conservation is still failing to utilise these advances. Whilst new technology enables conservation scientists to collect critical data for species population and health monitoring, constraints in human-processing capacity limits its use for research. AI offers a revolutionary alternative to manually labelling large datasets by running continuously and with consistent accuracy. AI models for wildlife identification and human medical diagnosis are developing rapidly, but remote disease detection and surveillance remains unexplored for conservation.

Here, we propose a project to automate the identification of leprosy (aetiological agent, Mycobacterium leprae) in western chimpanzees (P. troglodytes verus) using a large labelled camera-trap dataset from Cantanhez National Park (CNP), Guinea-Bissau. The student will be collaborating with experts in great ape behaviour and conservation who recently conducted pioneering research into multiple cases of leprosy across six communities of wild chimpanzees in CNP (https://www.nature.com/articles/s41586-021-03968-4). Individuals displayed different stages of the disease from loss of hair and facial depigmentation to nodules covering the face and extremities, and characteristic symptoms such as claw-hand. Existing models for species-level identification and individual (facial) recognition for chimpanzees will be re-trained, and a new model will be developed to recognise leprosy symptoms in individuals. With the ability to run both past and present images, the software will facilitate real-time analyses for remote field-work, to accelerate the diagnosis, monitoring and potential treatment of leprosy and other wildlife diseases with visible symptoms. Following model development, a pioneering study into the use of camera-trap data to automate disease detection in wildlife will be conducted; drawing techniques from value-of-information theory, we can quantify the value of such a software for conservation planning. The social implications regarding the use of AI in this project will also be considered to ensure a robust and ethical tool for practical conservation.

The project’s motivation is strongly rooted in its real-world application, which extends further than the scope of this project. Chimpanzees - a charismatic and endangered species - will be used to pilot these methodologies; however, such models would also allow the early detection of other visible diseases such as facial tumours on Tasmanian devils (Sarcophilus harrisii). The application of Deep Learning AI to identify the species, individual, and disease status of threatened wildlife will provide a major advance for conservation science, harnessing the power of computer science to conserve threatened species.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

Robin Chadwick, Senior Lecturer - University of Exeter

Ralph Trancoso, Adjunct Assosiate Professor, School of the Environment, University of Queensland

Additional Supervisors:

Jozef Syktus, Professorial Research Fellow, School of Biological Sciences, University of Exeter

Matthew Collins, Joint Met Office Chair in Climate Change, Department of Mathematics, University of Exeter

Project Description

The El Niño Southern Oscillation (ENSO) is the single most important global driver of interannual climate variability, with large impacts on weather extremes and climate across the world through what are known as atmospheric teleconnections (remote connections via the atmosphere). Therefore, any changes to ENSO or its teleconnections under climate change have the potential for severe global impacts on economy, society and the environment.

Australia is strongly affected by ENSO, experiencing typically dry conditions during warm-phase ENSO (El Niño) and wet conditions during cool-phase ENSO (La Niña) with consequences for agriculture, fires, water resources, and other sectors. In 2022, floods linked to La Niña devastated several towns across the Australian eastern seaboard. Likewise, several extreme droughts in the Australian past have been linked to El Niño. In Queensland, rainfall variability and changes associated with ENSO teleconnections have a strong impact on agriculture, water supply, ecosystem health and natural disasters.

Global warming is expected to increase the frequency of extreme ENSO events and recent studies indicate that this is likely to affect the pattern and intensity of teleconnections. However, it remains unclear how changes in ENSO will affect weather and climate around the world, and what impacts these will have on human societies and the natural world, which severely limits practical action on climate adaptation. Projections indicate an increased likelihood of stronger amplitude of extreme ENSO phases which will increase heat stress, bushfire risk, droughts and floods associated with opposite phases of ENSO i.e hot and dry (El Niño) or warm and humid (La Niña). Climate projections do not agree on the sign and magnitude of summer rainfall change in Queensland and this needs to be resolved in order to provide useful advice to decision makers.

This project will assess the response of ENSO teleconnections to climate change and their impacts on dry and wet conditions from a global-to-regional perspective, including a case study on Queensland. The project will use both state-of-the-art global climate models (GCMs) and more idealized climate models to investigate the physical processes that drive ENSO teleconnection changes in the tropics and subtropics. The impact of these changes on droughts and wetness will then be assessed at the regional scale with both GCMs (CMIP6) and high-resolution climate simulations. It is expected that this project will provide critical insights into the changing nature of ENSO teleconnections impacting rainfall across the tropics and subtropics, with a particular focus on Queensland.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

Ben Raymond, Professor of Ecology and Evolution - University of Exeter

Mark Schembri, Professor, Director IMB Centre for Superbug Solutions - University of Queensland

Additional Supervisors:

Professor Angus Buckling, Professor of Evolutionary Biology, University of Exeter

Dr Ben Temperton, Associate Proffesor of Microbiology, University of Exeter

Project Description:

Antibiotic resistance is a major emerging threat to global health. Escherichia coli is responsible for more antibiotic-resistant infections than any other bacterial species. Within E. coli a significant proportion of multi-drug resistant (MDR) infections are caused by a single lineage: the global epidemic clone Sequence Type (ST) 131. In both Australia and the UK ST131 causes more than half of all MDR bloodstream infections and is a major cause of urinary tract infections (UTIs)¹. The rising tide of resistance means that alternative means of treatment are urgently needed. Bacteriophages, viruses that kill bacteria, have a hundred-year history of treating infection, although for many decades only Eastern European countries dispensed phage at scale. Phage therapy is undergoing a global resurgence.  Multiple centres (including the Exeter) can produce phage cocktails aimed at compassionate treatment of severe drug-resistant infections.

Going beyond compassionate use of phage is another challenge and requires strategies that consider the potential for the rapid evolution of resistance. Here we propose to turn the evolution of resistance to an advantage: the focus of this project will be phage that use bacterial virulence factors (VFs) as binding sites. For VF-dependent phage, the evolution of resistance can be advantageous, as resistance commonly evolves through loss of expression of VFs. The resulting resistant, but attenuated, bacteria may be more susceptible to immune responses or can persist without causing disease. This pattern is well established for capsule-dependent phage and has been observed in vitro and in vivo^2,3. However, there has been no systematic effort to isolate phage that specifically target the VFs required for severe E. coli infections. This project will collate a phage bank that can target wild type ST131 and then screen these phage against VF deficient mutants to identify phage with lytic activity that depends on VF expression. We will then select for phage resistance in diverse media and re-sequence mutants to identify the putative basis of phage resistance. Conventional bacterial genetics will confirm the basis of resistance. Furthermore, using methods developed by MS we will use a library of transposon mutants to investigate the pathways behind the expression of VFs, information that will help us design more effective phage cocktails and better understand ST131.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

Timothy Holsgrove, Senior Lecturer Biomedical Engineering - University of Exeter

Justin Cooper-White, Professor - University of Queensland

Project Description:

Degeneration of the intervertebral disc (IVD) is commonly linked to low back pain, which is the leading global cause of years lived with disability. Traditional surgical approaches to treat degenerative disc disease, such as spinal fusion and total disc replacements have high costs, relatively poor outcomes and relatively high complication rates compared to other orthopaedic procedures such as hip and knee arthroplasty. Therefore, there is increasing interest in biological, and regenerative therapies such as the use of growth factors, cell injections, minimal repair strategies such as replacing the gel-like nucleus pulposus at the centre of the intervertebral disc, and the development of tissue-engineered discs to improve outcomes for patients. However, there is a limited understanding of how mechanical loading affects cellular activity and viability within the IVD, which is critical to understand the degenerative process, and for any interventions or regenerative strategies to be successfully implemented.

The overall aim of the project will be to develop and test an intervertebral disc-on-a-chip prototype with integrated microfluidic loading. The project will build upon previous and ongoing projects between the Exeter and UQ that have developed a six-axis bioreactor to replicate the complex mechanobiological conditions of the IVD in the laboratory environment, the use of this system to provide a greater understanding of how mechanical loading affects IVD cells, and the development and evaluation of regenerative therapies for the IVD.

The existing six-axis bioreactor uses a whole organ bovine intervertebral disc culture model, and is capable of subjecting discs to complex daily activities, but it is limited to only being able to test one IVD at a time. The disc-on-a-chip system will complement these existing test capabilities by increasing test throughput, which will improve efficiency and cost-effectiveness in the development and evaluation of new devices and therapies to treat degenerative disc disease.

The development of the disc-on-a-chip device will be completed in the Biomedical Engineering Group at the Exeter, which will provide access to the existing bioreactor system to inform the disc-on-a-chip design requirements, and complete development, and validation testing. The implementation phase will be completed in the Tissue Engineering and Microfluidics Laboratory at UQ, which allow the testing and evaluation of novel hydrogels and disc cell scaffolds that have been developed as part of their leading research into stem cell differentiation and tissue engineering.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

Dr Kathryn Moore, Senior Lecturer in Critical and Green Technology Metals - University of Exeter

Dr Juliana Segura-Salazar, Research Fellow (JKMRC & Development Minerals program) - University of Queensland

Additional Supervisors:

Professor Xiaoyu Yan, Professor of Sustainable Energy, University of Exeter

Project Description:

The low carbon transition is premised on the replacement of oil and gas wells with renewable energy infrastructure, the raw materials for which must be mined. An acceleration and expansion of mining activities places pressure on the environment, and it generates vast quantities of wastes where sought-after metals constitute only a small fraction of the rock. Simultaneously, extraction of sand from water bodies (e.g. rivers and coastal environments) creates significant negative impacts on working landscapes that support communities and ecosystems.

This PhD seeks to determine the conditions under which energy-critical metals and sands can be extracted at the same sites, relieving pressure on fragile ecosystems and fostering attempts to minimize climate change. Specifically the student will investigate co-production of Ore sands, a type of manufactured aggregate and silica sand that are sourced alongside mineral or metal ores. The ultimate goal is to place strong sustainability at the centre of models to define orebodies and thereby facilitate a mindset change from re-purposing of mining waste to characterisation of mining projects based on combined metal and ore sand co-production. The work has wider implications to reduce the risks associated with supply of raw materials, in alignment with government strategies.

The student will use case studies of the energy-critical metals of lithium (SW England) and copper (Pacific region), to constrain whether ore sands can be responsibly generated from contrasting  types of geological ore deposit. They will determine how analytical models that identify environmental and societal ‘hotspots’ in mining activities can be embedded in innovative business models and underpin policy for sustainability. The project requires the gathering of input data for modelling, including some mineral characterisation, excellent data organization and manipulation skills, and close collaboration with industrial partners.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

Patrick Foster, Associate Professor in Mine Safety - University of Exeter

Maurren Hassall, Director - Minerals Industry Safety &Health Centre - University of Queensland

Project Description:

Demand for Lithium Ion Batteries is expected to grow exponentially in the immediate future due to the increasing demand for electric vehicles. Other than lithium, a key component of such batteries is cobalt. There are significant health and safety challenges associated with the cobalt supply chain and it is listed as a restricted hazardous chemical under the Australian Work Health and Safety Regulations.

Total world reserves are estimated by the at around 7.6 million tons of contained cobalt. Cobalt is currently mined in several countries, but the Democratic Republic of the Congo is by far the largest producer. It is not a conflict mineral, but the DRC is a politically volatile country and it is extracted by artisanal and small scale mines as well as mines operated by major mining companies, such as Glencore. However, as demand rises, mining in other countries including the US, Canada, and Australia is set to increase (Cobalt Institute, 2023). In addition, it is anticipated that cobalt will be sourced from reprocessing mine tailings and recycling of batteries and other cobalt containing materials using novel mineral extraction processes.

NGO's have been leading a global campaign to exert pressure on EV manufacturers to ensure that their cobalt supply chain is ethical (Amnesty International, 2016). Manufacturers are being forced by such NGO activity and increasing consumer awareness to respond to these supply chain concerns and demonstrate positive corrective action. One of the major concerns is workplace and working conditions, as risks related to OHS are more prevalent than human rights abuses and conflict financing among global artisanal and small-scale miners as an example (mining.com, 2019).

The aim of the study is to identify specific OHS risks in the ethical cobalt supply chain and formulate suitable governance frameworks to manage those risks and minimise supply chain disruption. This will help mining companies and downstream manufacturers protect their bottom line and reputational risk, whilst satisfying increasingly ethically minded consumer base and NGO’s.

How to Apply:

Award details | Funding and scholarships for students | University of Exeter

Supervisors:

UQ - Dr Ivano Bongiovanni

Exeter - Dr Lewys Brace

Project Description:

The growth, in recent years, in the usage and applications of massive language models (MLM) has been nothing short of exponential. Proprietary MLMs such as ChatGPT, BERT, and Bard have found widespread application in various domains, all associated with efficiently writing content of various nature. At the same time, several open-source MLMs have demonstrated efficiencies comparable to that of their proprietary equivalents. MLMs have been employed for natural language processing tasks like language generation, text summarization, and sentiment analysis, and have dramatically changed the ‘world’ of chatbot systems, virtual assistants, and machine translation services. The explosion of MLMs has transformed the way we interact with language and opened up new avenues for innovation and exploration. On the flipside, their evolution also presents undeniable challenges, associated with malicious usage, ethical considerations, and biased outputs.

Deepfake technology has evolved in parallel to MLM. Deepfakes involve the use of artificial intelligence algorithms to create realistic, manipulated media content, including images, videos, and audio. While initially known for creating realistic face swaps, deepfake technology has found applications in entertainment, social media, and even political discourse. However, it also poses significant challenges, including the potential for misinformation, identity theft, and erosion of trust. Detecting and mitigating deepfakes remains an ongoing challenge, necessitating advancements in detection algorithms, media forensics, and public awareness to address the potential negative impacts of this technology.

At the intersection between MLMs and deepfake technology reside significant challenges in the realm of cybersecurity, stemming for the possibility, for cyber-criminals, to perpetrate chatbot-based cyber-attacks at scale with greater sophistication and effectiveness. For example, what happens to an organisation that is using a chatbot as its "public face" but then a cyber-criminal hijacks the chatbot (replicating it and using it for an unrelated or loosely related attack at scale)? Consequences could include significant reputational damage, harm to the end-users and large-scale losses in general.

In this scenario, at this stage, we can only but hypothesise possible countermeasures for organisations to face a threat landscape in which cyber-attacks are perpetrated more intensely, faster, and more effectively. What strategies are required to protect organisational assets from a potentially large-scale influx of cyber-attacks?

In this research proposal, we aim at investigating the integration of MLMs and deepfake visuals in cyber-attacks, to advance our understanding of this emerging threat landscape and lay the foundations for effective countermeasures by organisations from the public and the private sector.

This interdisciplinary project addresses three research questions:

1. How can deepfakes be integrated with MLMs to create chatbots that mimic the appearance and behaviour of real individuals?
2. What are the potential risks associated with the MLMs and deepfake integration, particularly in the context of social engineering, disinformation campaigns, and phishing?
3. What can organisations and policymakers do to protect individuals from threats that appear effective, inexpensive, and hyper-sophisticated?

How to Apply:

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)

Supervisors:

UQ - Associate Professor Stan Karanasios

Exeter - Associate Professor Andreas Wihler

Project Description:

Artificial intelligence (AI) and its applications are without any doubt rapidly evolving and transforming workplaces.

This domain involves two different yet connected streams, namely automation via machine learning and augmentation via AI. But although many disciplines define AI-based augmentation as technology and humans working together to jointly perform tasks, applying these definitions to organisational design and work practices has not been straightforward.

Exacerbating this issue, most of the research on AI in organisations focuses on the negative impact of primarily automation and how the subsequent loss of jobs can be mitigated. The emphasis on automation and the implied loss of jobs has resulted in a narrow research perspective that predominantly focuses on how to reskill employees and increase job autonomy. Consequently, automation has mainly been studied in mechanical jobs that are prone to substitution by robots, but far less in the area of professional services or other jobs where machine learning and the use of AI are on the rise.

However, the rapid development of machine learning and AI highlights that we have little understanding of the impact of AI on the more generative aspects such as the co-creation and interactions with individuals in their work. This is particularly so across a range of different knowledge work that involves tasks that require a degree of specialized knowledge or skill, often involving handling or using information. Examples include lawyers, consultants, medical professionals, researchers and data analysts. Knowledge work is experiencing drastic changes in the application of AI that will deeply affect the professions in ways that go beyond the automation-debate and may have a significant impact on professional identities. Thus, one of the project’s goals is to unpack what augmentative human-AI models of working in organisations look like and to illuminate what it means for organisational design and work.

Contributing to the field of information systems and organisation studies research, the project will be led by the Business Information Systems discipline at the University of Queensland. This project is also linked to and supported by the Exeter Business School’s DIGITLAB which focuses on organisational practices of digital transformation. Adopting an interdisciplinary perspective, the project has the following goals, which will be addressed using a combination of qualitative and quantitative methods such as case studies, interviews and surveys:

1. Integrate current definitions from different disciplines and applied examples of augmentation to illuminate current models within organisations.
2. Understand augmentation in practice in the knowledge work by examining how workers use, work in collaboration with, and respond to AI intended to augment their work.
3. Investigate how job and organisational roles of individuals change based on AI-augmentation. The project will focus on how augmentation can affect and shift organisational tasks within one job, identifying antecedents and contingencies of augmentation patterns and relating these to individual and organisational effectiveness.

The project will be collaboratively supervised by Associate Professors Stan Karanasios, Stephen Viller and Ping Wang from the University of Queensland and Associate Professor Andreas Wihler and Professors Ilke Inceoglu and Leroy White from Exeter University.

How to Apply: 

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)

Supervisors:

UQ - Professor Anthony Richardson

Exeter - Dr Kristian Metcalfe

Project Description:

We use the ocean in many diverse ways – including fishing, aquaculture, mining, oil and gas production, aggregate extraction, shipping, renewable energy generation, and tourism – all of which threaten marine life. To protect biodiversity and the ecosystem services they provide, countries have recently ratified the Montreal-Kunming Global Biodiversity Framework, where they have agreed to increase protection targets to 30% of the planet by 2030, including in the oceans (the 30x30 initiative). How we meet these biodiversity protection targets whilst not compromising the human activities we depend upon is an urgent scientific question. This is even more challenging in the Global South, where because of their current low levels of biodiversity protection, will need to expand their protected area estate even more than the Global North (Metcalfe et al., 2022 Cons Lett).

The relatively few examples of zoning human activities in the ocean – known as multiple-use marine spatial planning use complex ecosystem models (e.g., Atlantis, Olsen et al. 2018 Front Mar Sci) or conservation planning software that are a black box to stakeholders engaged in the process (e.g., Marxan with Zones, Watts et al. 2009 Env Model Soft). Recently, a new package in R has been developed, prioritizr, which can be used for multiple-use spatial planning, solve a much broader range of spatial planning problems, is orders of magnitude faster, allows a transparent workflow to be developed in R, and facilitates the development of R Shiny Apps for communication of results with stakeholders.

This project will develop the science needed to meet global conservation targets whilst optimising human benefits through maximising co-benefits (ecosystem services) and opportunities afforded by co-location of activities. We will develop novel methods for incorporating multiple sets of values into conservation planning, and mechanisms to allow stakeholders to share and explore other value sets during the planning process. We will engage with and apply our methods and tools to real-world projects in developing nations in West and Central Africa and island nations of the Indo-Pacific through collaboration with two existing projects. UQ leads a Waitt Foundation Project developing spatial planning approaches and tools for Indo-Pacific Island nations; Exeter leads a project delivering similar tools in West and Central Africa.

How to Apply:

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)

Supervisors:

UQ - Professor Gary Schenk

Exeter - Associate Professor Nicholas Harmer

Project Description:

The bioeconomy of the 21st century is defined by an increasing urgency to develop environmentally friendly, sustainable production pathways for the conversion of renewable raw materials (e.g. carbohydrates and fats) to high-value chemicals. Synthetic biology has emerged as a powerful approach to optimise metabolic pathways to produce high-value chemicals from these feedstocks. While considerable progress has already been made with cell-based systems, their efficiency, controllability and longevity can be limited by operational conditions (e.g. temperature and pressure) and the toxicity of end products to the engineered organism. To bypass these limitations, cell-free, enzyme-based approaches have gained increasing attention as pipelines for the synthesis of high-value chemicals. The catalytic efficiency and stability of naturally occurring enzymes are rarely optimal for use in an engineered multi-enzyme cascade. Enzyme properties are therefore tailored for a required need through methods such as in vitro evolution (as recognised by the 2018 Nobel Prize in Chemistry). The significance, benefits and innovation of this project are centred on an optimised cell-free biomanufacturing platform for the rapid production of a wide range of chemicals. Specifically, the project will enhance the potential of cell-free biomanufacturing to transform industrial processes of the emerging sustainable bioeconomy.

This project will optimise cell-free cascades for the production of the alcohols ethanol and isobutanol from sugar cane bagasse and grass silage (QUEX scheme “Global Environmental Futures”). The selection of the products is guided by UN sustainable development goals, in particular goals 7, 9 and 13. Ethanol and isobutanol are the only certified alcohols that can be used as drop-ins for aviation fuel, thus playing a central role in transitioning the aviation sector toward sustainable processes. Importantly, the proof-of-concept of the required enzyme cascades has already been demonstrated by our collaborator from the Technical University of Munich (Prof. Volker Sieber). The proposed supervisory team from the two partner universities will use their expertise in enzyme structure, function, and mechanism studies to optimise the individual enzymes of the cascades and will employ in silico modelling and machine learning approaches to maximise the productivity of the cascades. The outcome of the project will be a blue-print to scale up these processes to industrial scale, which will contribute to the decarbonisation of a rapidly growing industry sector.

How to Apply:

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)

Supervisors:

UQ - Dr Jenna Taylor

Exeter - Dr Bert Bond

Project Description:

Ageing impairs vascular health in men and women, although the trajectory is different between sexes due to the early protective effects of oestrogen on cardiovascular health. Consequently, the loss of oestrogen during menopause, results in a rapid and significant decline in vascular health, increasing the risk of cardiovascular disease and stroke. Reductions in cognitive function also occur during menopause, which has implications for the risk of dementia.

Exercise training has been shown to help oppose the detrimental effects of ageing and the menopause transition on cardiovascular health, as well as reduce the severity of vasomotor symptoms or “hot flushes”. However limited data exists on whether these benefits also translate to cerebrovascular health and cognitive function. Furthermore, the optimal exercise prescription for vascular adaptations in women during menopause is currently unknown. High-intensity interval training (HIIT) involves alternating bouts of high-intensity exercise with bouts of low-intensity recovery. Our research group has previously found HIIT to provide superior improvements in vascular function when compared with moderate-intensity training for patients with cardiovascular disease and non-specific populations. However, women have historically been understudied and more targeted exercise training research is warranted to better understand specific approaches for women, particularly during the menopause transition.

This PhD studentship intends to investigate the effect of exercise training and intensity on cerebrovascular and peripheral vascular function in women going through the menopause transition; and explore associations between changes in vascular function and vasomotor symptoms.

This question will be investigated through a randomised interventional exercise training study across both Queensland and Exeter sites, with the novelty of incorporating measures of cerebrovascular physiology. In so doing, the collaboration will enhance the research capacities of both laboratories (Queensland and Exeter) within the field of “healthy living”. This new knowledge and shared expertise will provide an excellent foundation and preliminary data to attract future funding for multi-centre studies relating to ageing and primary prevention of dementia and cardiovascular disease.

How to Apply:

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)

Supervisors:

UQ - Professor Philip Hugenholtz

Exeter - Professor William Gaze

Project Description:

Intensive use of antibiotics over the last decades has resulted in widespread antibiotic resistance in microorganisms. The overall goal of this project is to characterise the evolution of metallo-β-lactamase (MBL) activity in ribonucleases, which is a novel and unexpected source of resistance to front-line drugs of last resort. Insights gained will provide the foundation for the design of new therapeutics to reduce the spread of resistance by identifying relevant amino acid residues and structural features that contribute to the change of functionality from that of a nuclease to a β-lactamase. Such residues and structural features will enable subsequent drug design programs since they provide precise definition of previously unrecognised regions to target with specific inhibitors.

β-Lactams include the most widely used antibiotics (e.g. penicillin) as well as antibiotics of last resort (carbapenems) but their efficiency is decreasing due to a rapid increase in microbial resistance globally. A series of key pathogens (e.g. Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa) express β-lactamases to inactivate these antibiotics. There are two major groups of β-lactamases, the serine-β-lactamases (SBLs) and the MBLs. This project is designed to define the essential elements (amino acids, structural regions) that contribute to MBL function and to investigate selection for MBLs using experimental evolution. These elements will provide a novel focal point for the development of MBL-specific inhibitors, in particular to overcome carbapenem-resistant Gram-negative bacterial infections as identified in the WHO antibiotic-resistant priority pathogens list.

This approach builds on recent foundational research from our team and others including: 1) the discovery that four evolutionary-related MBL class members (from subgroup B3) have different substrate preferences and responses to β-lactam-based inhibitors, thus confirming their structural and functional plasticity; 2) the discovery of bacterial, archaeal and virally-encoded RNaseZ and β-CASP nucleases that are homologous to B3 MBLs and exhibit both nuclease and MBL function; 3) the observation that the loss of the β-CASP domain of the β-CASP nucleases enhances their MBL activity, thus increasing resistance to carbapenems (unpublished data). This project will map amino acid mutations that have occurred in RNaseZ and β-CASP nucleases to enhance β-lactamase activity, thus identifying essential features for the antibiotic-degrading activity. The project has been structured into three specific aims:

1. The “present” - Map the evolution, distribution and diversity of existing B3-type MBLs, RNaseZ and β-CASP nucleases.
2. The “past” - Reconstruct ancestral genes encoding precursors of existing B3-type MBLs, RNaseZ and β-CASP nucleases.
3. The “future” - Select for mutations that convert RNaseZ and β-CASP nucleases into efficient MBLs.

Contact

How to Apply:

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)

Supervisors:

UQ - Professor Daniel Franks

Exeter - Dr Robert Fitzpatrick

Project Description:

Each year, billions of tonnes of mining wastes, such as tailings and waste rock, are generated and stored in dams or stockpiles are discarded directly into the natural environment. As mining is expected to play a significant role in the clean energy transition, future extraction and processing of energy-critical minerals based on the current ‘take-make-use-dispose’ model will continue to add to the problem and exponentially increase the production of mineral waste over the next decades, reaching volumes in the order of a trillion tonnes. These waste materials pose significant environmental and social risks while they represent a missed opportunity to adopt resource-efficient and sustainable practices.

Instead, proactive waste reduction through innovative eco-design of mining operations can maximise the utilisation of minerals in ores and transform them into value-added materials such as ore-sand before they become waste. This approach can help alleviate the exponentially growing demand for sand in construction and other applications. Sand, gravel and crushed stone are extracted at a rate of more than 50 billion tonnes annually and the United Nations Environment Programme is increasingly concerned about the impacts of this extraction, especially when sourced from dynamic ecosystems like rivers, beaches and the marine environment.

Focusing on case studies in Australia and the United Kingdom, this project aims to explore the technical feasibility and sustainability of co-producing ore-sand from energy-critical ores. The project has a strong experimental focus and involves the use of characterisation techniques for ores/materials, evaluation of material’s compliance with technical standards, technical assessment of the potential uses of ore-sand and other materials co-produced at laboratory/pilot scale, development of novel, eco-efficient flowsheets, and sustainability assessment through life-cycle analysis. The project outcomes will contribute to developing a compelling business case, serving as a foundation for transitioning towards a strongly sustainable and truly circular business model.

How to Apply:

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)

Supervisors:

UQ - Associate Professor Steven Micklethwaite

Exeter - Professor Karen Hudson-Edwards

Project Description:

There are an estimated 80,000 disused mine sites in Australia alone, with ~66,000km2 of land occupied by mine waste landforms globally (Tang & Werner 2023). This is an enormous liability, with long-term impacts on groundwater, biodiversity and human health that require costly programs of intervention. Yet, there is growing recognition that mine waste also presents an opportunity for circular economy, particularly in the reprocessing of waste material for critical metals and development minerals.

A key question then, that society and government agencies are facing, is how do we accurately survey such vast numbers of sites in order to make informed decisions around their environmental footprint and potential for recycling? There are outstanding needs to directly detect critical metals, map acid mine drainage (AMD) and disbursement of toxic metals, and measure rates of erosion in legacy sites.

Conventionally, ground-based sampling has been used but this is expensive and misses the spatial context. Satellite-based, multi- and hyperspectral infrared imaging (MSI and HSI respectively) provide the spatial context but they are also hampered. Although they have the potential to identify signatures of AMD and detect certain critical metals, recent work from members of this project has shown the spectral and pixel resolutions of satellite remote sensing are not sufficient (Chalkley et al., 2022).

This project addresses the question by investigating the application of exciting, new, drone-based HSI sensing. The technology covers the visible near-infrared (VNIR) and shortwave infrared (SWIR) spectrums at unprecedented high resolution, and it opens opportunity for change detection via cost-effective repeat surveys. We will survey key tailings sites in Queensland in collaboration with the Department of Resources. The efficacy of the technique will be validated against ground samples and the student will develop the analytical approaches, frameworks and best-practise for mapping mine waste. Critically, the student will then have opportunity to develop machine vision classification approaches for the rapid generation of AMD, erosion and critical metal maps, and to then better constrain satellite data so that it can be used to more accurately map larger areas.

How to Apply:

QUEX PhD Scholarship - Scholarships - The University of Queensland (uq.edu.au)