Skip to main content

Applications for 2020/21 entry are now open

The University of Exeter and The University of Queensland are seeking exceptional students to join a world-leading, cross-continental research team tackling major challenges facing the world’s population in global sustainability and wellbeing as part of the QUEX Institute. The joint PhD scholarship program provides a fantastic opportunity for the most talented doctoral students to work closely with world class research groups and benefit from the combined expertise and facilities offered at the two institutions.

Eight generous, fully-funded scholarships are available for the best applicants, four offered by the University of Exeter and four offered by The University of Queensland. This select group will have the chance to study in the UK and Australia, and will graduate with a joint degree from both the University of Exeter and The University of Queensland.

Summary

Application deadline:

31 August 2020 (BST)

Value:

Full tuition fees, stipend of £15,000 p.a, travel funds of up to £15,000, and RTSG of up to £15,000 are available over the 3.5 year studentship

Duration of award:

per year

Contact: PGR Admissions Office

pgrenquiries@exeter.ac.uk

How to apply

You will be asked to submit some personal details and upload a full CV, supporting statement, academic transcripts and details of two academic referees. Your supporting statement should outline your academic interests, prior research experience and reasons for wishing to undertake this project, with particular reference to the collaborative nature of the partnership with the University of Queensland, and how this will enhance your training and research.

Applicants who are chosen for interview will be notified week commencing 28 September 2020, and must be available for interview week commencing 12 October 2020.

PhD start date is expected to be April 2021.

Please clearly quote the project reference number on your application and in any correspondence about this studentship.

 

University of Exeter projects

Supervisors:

Dr Mathilde Pavis, Senior Lecturer; Director of Undergraduate Studies (LLB Director), University of Exeter

Dr Lisa Bode, Senior Lecturer in Film and Television Studies, University of Queensland

Project Description:

Can ‘Deepfakes’ be good? What does ‘good’ or ‘bad’ persona appropriation created with artificial intelligence, such as Deepfakes, look like?

This interdisciplinary PhD research project proposes new ways of governing the practice of persona appropriation created with artificial intelligence (AI) to rebalance public and expert debates on this topic. To achieve this ambitious aim, this PhD studentship will be dedicated to developing an innovative critical framework which will serve as a tool for assessment of positive and negative persona appropriation within a spectrum of harm and benefits.  Persona appropriation refers to the act of manipulating, modifying, adding or erasing aspects of an individual’s persona. Persona can be broadly defined as the representation of a person’s identity through the imitation of their image, voice or likeness. The latest and most challenging form of AI-made persona appropriation is colloquially known as ‘Deepfakes’. Deepfakes refers to the synthetic content produced using artificial intelligence; they are a type of AI-made persona appropriation. Deepfakes most commonly take the form of fabricated audio-visual footage of a person created using existing (authentic) footage edited with an AI algorithm to produce more realistic and high-quality results. This project focuses on synthetic audio-visual content, as the most cutting-edge application of the technology in the context of persona appropriation. The reader is invited to view examples of Deepfakes here: https://tinyurl.com/PAVISDEEFAKE . Deepfakes have revived public and expert interests in tackling harmful forms of persona appropriation to the extent that countries like Australia, the United States or France are implementing or debating new legislation. The industry is also investing in detection programs and developing best practice principles. Yet, these conversations have been overly focused on abusive forms of persona appropriation at the cost of a more balanced and informed approach that could account for both negative and positive applications of AI in this context. The research project fills this gap in knowledge to produce sustainable governance principles for AI-made persona appropriation.

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3895

 

Supervisors:

Professor Guangtao Fu, Professor of Water Intelligence, University of Exeter

Dr Alina Bialkowski, Lecturer in Computer Science, University of Queensland

Project Description:

Natural disasters are among the world’s greatest challenges and 80,000 people per day are affected with an economic loss of US$ 1.5 trillion since 2003. Flooding alone, which is the most frequent and wide-reaching weather-related natural hazards in the world, has affected 2.3 billion people with an estimated economic losses of US$ 662 billion from 1995 to 2015, and US$ 60 billion in 2016 alone. In both UK and Australia, the impacts of floods and droughts are projected to increase in the future due to climate change, population increase, and aging water infrastructure and lack of investments. 

This project aims to develop a deep learning approach and tool for rapid, large scale assessments of the impacts of natural hazards with an aim to optimizing emergency planning and operation. Its main objectives are to 1) build open source datasets of natural hazards from multi-sources such as satellite imagery, CCTV images and social media, 2) develop a deep learning algorithm based on convolutional neural networks (CNNs) to detect impact extent and vulnerable objects such as human or cars, 3) use reinforcement learning to develop effective emergency responses considering system interdependencies and people behaviours, 4) analyse the impact of people behaviours on emergency planning. This project will focus on the following three natural hazards: floods, droughts and bushfires. The outcome of this research will be fed directly into emergency planning and response in order to reduce the risks of natural hazards, and will be tested in real-world scenarios through collaborations with our industrial partners. For example, in the recent event of Storm Dennis, a woman was trapped on the roof of her submerged car for 12 hours before being rescued in England. This tool will be able to identify such situations using remote sensing or CCTV imagery.   

This project will be supervised by a strong team with significant research strengths in natural hazards and artificial intelligence (AI) research, based at the University of Exeter and the University of Queensland. The student has a unique opportunity to access the resources and facilities at the national institute for data science and AI – Alan Turing Institute. 

The timeliness and originality of this project lie in the following aspects: 1) building on the latest breakthroughs in deep learning, this project will develop new AI algorithms to tackle a pressing global challenge, i.e., to reduce the damages of natural hazards; 2) the topic of this project is well aligned to the research priorities of the Institute of Data Science and Artificial Intelligence, the QUEX Institute and the Alan Turing Institute.

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3896

 

Supervisors:

Dr Rich Crane, Lecturer in Sustainable Mining, University of Exeter

Dr Anita Parbhakar-Fox, Senior Research Fellow, University of Queensland

Professor Karen Hudson-Edwards, College of Engineering, Mathematics and Physical Sciences, University of Exeter

Project Description:

The problem:

Acid mine drainage (AMD) is an acidic solution created by the oxidation of sulphide minerals, typically associated with areas of legacy metalliferous and coal mining. It is an extremely widespread environmental issue, and often ranked alongside climate change, microplastics and ocean acidification in terms of global ecological risk. 

In the UK the vast majority of mining activity ceased several decades ago, however, as much as 6% of all surface water bodies are still currently adversely affected by AMD. A similar but larger scale scenario exists in Australia where widespread historic and current mining activity has resulted in a total annual AMD management cost of approximately $500m AUD. 

Despite such widespread environmental and financial cost AMD often contains dissolved metals and metalloids (hereafter metals) which would be beneficial to recover (e.g. Fe, Cu, Ni, Zn). An intrinsic barrier, however, is that they are often present at relatively low concentrations (e.g. <10 mg/L) and as such their recovery and conversion to bulk and/or sheet metal is not typically economically viable.

The solution:

This PhD project will focus, for the first time, on the integration of electrokinetics, physical forces (namely: microwave energy and ultrasonic energy) and/or low concentration reagents (emulsifiers, chelating agents, complexing agents, etc.) in order to develop next generation field deployable endof-pipe “modules” for the selective and precise self-assembly of functional nanomaterials from AMD, using minimal (or ideally zero) chemical additives. This process, known as “upcycling” will, if successful, enable the direct conversion of metals within AMD into high value products and thereby unlock an entirely new economic incentive for such AMD treatment. 

Alignment with QUEX and the UN Sustainable Development Goals:

The project is closely aligned with the QUEX “Global Environmental Futures” and UN Sustainable Development Goals: (6) Clean Water and Sanitation; (9) Industry, Innovation and Infrastructure; and (14) Life Below Water. In particular, the research will strive towards development of new technology in order to transform a globally significant and retractable environment problem into a new resource. Close involvement of Steven Morris (DEFRA, UK), Hugh Potter (EA, UK).

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3897

 

Supervisors:

Professor Kevin J Gaston, Environment and Sustainability Institute, University of Exeter

Professor Richard A Fuller, School of Biological Sciences, University of Queensland

Project Description:

Images of the Earth at nighttime are particularly striking in the way that artificial lighting apparently outlines so much of the coastline of the major landmasses. This lighting, arising from industrial, commercial, municipal, and domestic sources, constitutes a major pressure on coastal ecosystems that are vital to the wellbeing of a high proportion of the global human population and which are key biodiversity hotspots. It influences the individual behaviour of a wide diversity of organisms, changes species abundances, distributions and interactions, and alters the functions and services provided by these ecosystems. Indeed, some of the most infamous negative ecological impacts of artificial nighttime lighting are on coastal ecosystems, including the fatal disorientation of hatchling marine turtles and fledgling seabirds.

Yet the nature, extent and trends in the artificial lighting of coastal ecosystems have remained poorly characterised, and how the situation might be improved (for a better global environmental future) has been little explored. Resolving these knowledge gaps has recently become possible through developments, including pioneering work respectively by the supervisors of this project, in (i) mapping through time the intensity and spectrum of both direct artificial lighting emissions and resultant skyglow, including through use of imagery from the International Space Station; and (ii) mapping through time the extent of different coastal ecosystems, including tidal flats using machine learning to interpret nearly a million satellite images.

Much of the artificial nighttime lighting experienced by coastal ecosystems is a consequence of poorly designed or installed lighting systems that result in light being received in places where it is environmentally damaging but provides little or no human benefit. The negative impacts of artificial lighting on coastal ecosystems could doubtless be reduced at the same time as making significant economic and energy savings and reducing CO2 emissions. However, how readily this could be done has not been determined.

This studentship will: (i) Use remotely sensed data to document globally the spatial and temporal dynamics of artificial nighttime lighting of coastal ecosystems.

(ii) Develop and ground-truth an approach to quantifying the artificial illumination of horizons, which can be particularly important in coastal ecosystems.

(iii) Determine the potential for restoring the natural nighttime of coastal ecosystems by realistic steps to limit the trespass of lighting into areas in which it is not required.

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3898

 

Supervisors:

Dr Benjamin Wall, Associate Professor, Sport and Health Sciences, University of Exeter

Professor Ben Hankamer, Institute for Molecular Bioscience, University of Queensland

Project Description:

Although dietary protein is of crucial importance for human nutrition (particularly within sports nutrition and in support of healthy ageing), our information undergirding dietary protein guidelines and recommendations comes almost exclusively from research carried out with animal-derived proteins (Wall et al. 2014). Given we live in rapidly increasing (in number), ageing, wealthier and more urbanised populations, paired with the scientific community calling for increases in dietary protein recommended daily intakes (RDI) in various populations (Wall et al. 2014), the global demand for protein production is set to rise considerably in the coming decades.

Various major organisations and government bodies, including the University of Exeter, have declared a climate emergency. One of the major action points unanimously proposed to reduce carbon emissions is reduced reliance on the environmentally costly production of animal-derived protein sources. It is therefore of urgent importance to identify a range of potential non-animal derived, sustainable dietary protein sources and develop an evidence base for their efficacy within human nutrition.

Though numerous more sustainable alternative protein sources have been proposed as having potential to support a sustainable food future (e.g. various plant, insect or fungal derived protein sources) one as yet untapped source is algae. The present project takes a multidisciplinary approach to exploring the potential for algae as a dietary protein source to support human nutrition. 

The early part of the project, based in the University of Queensland, Australia, will utilise a biosciences approach to optimising processes to obtain a protein rich extraction from algae that is fit for human consumption. The second half of the project will be an in vivo metabolic physiology approach where the aforementioned algal protein sources will be fed to human volunteers and the digestion and absorption kinetics assessed as well as subsequent metabolism of the dietary derived amino acids. The latter experiments will be performed in both young and older adults, within the context of resting and exercised skeletal muscle, with a focus on the potential application of algal protein to support sports nutrition and active healthy ageing. 

REFERENCES:  Wall et al. 2014 Sports Med 44:185-94.

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3899

 

Supervisors:

Professor Celia Morgan, Professor of Psychopharmacology, University of Exeter

Professor Leanne Hides, Lives Lived Well Professor, University of Queensland

Project Description:

Cannabis use is rising worldwide due to changes in the legalisation in recreational and medical use. The growing value of the cannabis market means that tremendous pressures are being placed on policy makers by industry, and on consumers by increasing cannabis availability and potency. This is all in a landscape where there is an absence of clear understanding of what dictates vulnerability to its harmful effects and which are the best approaches to treating the estimated 10% of people for whom cannabis use becomes problematic.  Under the healthy living theme of the QUEX partnership this project will aim to understanding individual differences in risk of mental health and cognitive deficits associated with cannabis use, and how this differs for adolescents and older adults. It will examine individual differences in response to treatment for cannabis use disorder and barriers to accessing treatment and also explore how changes in legal status might affect mental health outcomes from smoking cannabis. 

The QUEX partnership affords the unique benefits of working both in U.K. and Australia, two countries with differing policies on cannabis use. This studentship will capitalise on three large existing datasets held by the lead supervisors to investigate 1) how age affects risk of negative outcomes particularly concentrating on two crucial periods in the life course - adolescence and older age - the latter of whom who have the fastest rising cannabis use worldwide; 2) how individual differences affect treatment for cannabis use disorder and barriers to accessing treatment; and 3) how changes in legal policy affect mental health consequences of using cannabis. Data from the Headspace Centres for adolescents in Australia will form the basis for investigating treatments of cannabis use amongst adolescents. 

Prof Morgan at University of Exeter has been collecting data on adolescent cannabis use over a number of years as part of a number of undergraduate and postgraduate student projects, in collaboration with researchers in Canada (UBC) and Israel (University of Tel Aviv). Prof Morgan is also using the PROTECT dataset of ~20,000 older adults hosted at the medical school at UoE to investigate the impacts of cannabis use in this age group. Prof Hides at UQ has been collecting data on cannabis use in ~5000 young people, with 748 enrolled into an RCT with follow up data at 3, 6, 9 and 12 months. Prof Hides also works together with Lives Lived Well, the largest support service for substance use problems across Queensland and New South Wales, and is collecting outcome data for all service users who enter treatment with LLW.

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3900

 

Supervisors:

Dr Stefano Pagliara, Senior Lecturer, Living Systems Institute and Biosciences, University of Exeter

Dr Mark Blaskovich, Institute for Molecular Bioscience, University of Queensland

Project Description:

Bacterial infections are one of the leading causes of death worldwide and the ongoing antibiotic resistance crisis currently costs the NHS over £180 million per year. The magnitude of this problem has been recognised by governments and funders across the Globe. The aim of this project is to determine the mechanisms that allow a cell to reduce the internal accumulation of molecules including drugs. This knowledge will lead to new ways of manipulating the membranes of pathogens to improve pharmaco-therapy.

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3901

 

Supervisors:

Richard Smith, Deputy Pro-Vice Chancellor and Professor of Health Economics, University of Exeter

Amanda Lee, Professor Public Health Policy, School of Public Health, University of Queensland

Project Description:

There is a popular perception, based on some evidence, that healthy food costs more than unhealthy food, and since price is a major determinant of demand, that addressing this price imbalance will rebalance consumption of healthy versus unhealthy foods. This has been the rationale behind the high-profile rise of various ‘fat tax’ and ‘sugar tax’ initiatives in recent years. 

However, when the cost of the whole diet is considered, evidence from Australia found that healthy diets could be less expensive than current (unhealthy) diets. There is concern, also, that taxing specific products does not necessarily lead to overall healthier dietary patterns. These observations cast doubt on the relative importance of price as the critical driver of food choice in the context of the whole diet, and this remains a significant gap in evidence underlying related health and fiscal policies. The assumption from which this study departs is that demand is affected not only by price but also by consumers’ knowledge, attitudes, behaviours, and socio-demographic characteristics, as well as by food availability. 

Prices are key contributors for demand, and while some evidence backs the popular perception that some healthy food items are more expensive than their unhealthy counterparts, studies of the total cost of diets, rather than foods per se, may be required given the challenges around study design and statistical coupling that affect some approaches. Under Australian fiscal policy settings, healthy diets can be up to 15% less expensive than unhealthy diets. 

Prices are not the only factor shaping food demand. Therefore, the analysis will also consider additional elements, such as access, food preferences and values, ‘taste’, food literacy, ability to prepare and store foods, perceptions of ‘healthiness’ etc. Consumer attitudes may be responsible for many of the final food choices made. Those attitudes are affected by culture and socioeconomic backgrounds, as well as by marketing strategies, and other environmental conditions.  

The aim of this project is to add to the evidence base around food and diet costs and drivers of food choice.  Specifically to: (i) establish if the findings in Australia are unique or replicated elsewhere, in this case the UK; (ii) establish the relative importance of price as a driver for food choice vis other factors of known importance (income, price of other products, convenience, access, knowledge, preferences etc.) in a cross-country comparison. These will be unique contributions to the literature in this area.

How to Apply:

http://www.exeter.ac.uk/studying/funding/award/?id=3902

 

Supervisors:

Professor Matthew Dargusch, Lecturer in Mechanical and Manufacturing Engineering, University of Queensland

Professor Gavin Tabor, Professor of Computational Fluid Dynamics, University of Exeter

Project Description:

Industry 4.0 is a group of disruptive technologies that is transforming manufacturing and has the potential to revolutionise the manufacture of a range of high value products leading to significantly improved more efficient and more sustainable designs. Two of the key technologies underpinning Industry 4.0 are Digital Simulation and Optimisation (particularly including Machine Learning) and Additive Manufacture (AM). A Digital Twin is a computational model of a device which can be used to simulate and optimise its performance, without recourse to building expensive physical prototypes. Additive Manufacture is a cutting edge manufacturing process which builds devices up layer by layer (often through extrusion of a rapid-setting material, i.e. 3d printing, or by using lasers to fuse powder together in Selective Laser Sintering (SLS). This is enormously more efficient than traditional manufacturing and can be used to produce significantly more complex devices as well as tailoring them to specific needs. An example is the design of complex heat exchangers and heat exchange surfaces. Traditional heat exchangers manufactured by moulding and fitting together machined components, are limited to use a few large heat exchange surfaces such as pipes, leading to large sizes and relative inefficiencies. An AM heat exchanger could use hundreds of small tubes each carefully designed to optimise heat transfer. Such a system with its light weight and high efficiency could be useful in many applications, but most notably in fuel cells for power generation from H2 gas in a range of transport applications such as cars and aircraft. This is of significant interest in transforming transport options from fossil fuels to renewable systems.

The project brings together world-leading expertise in Exeter (Prof Tabor: Computational Fluid
Dynamics, Optimisation with Machine Learning and Adjoint Optimisation) and the University of
Queensland (Prof Dargusch: metal-based Additive Manufacture and Smart Manufacturing). In the 1st year in Exeter, the PhD student will work on developing a Digital Twin model of the heat exchanger and investigate various interior geometries for the heat exchange surfaces. This will continue in the 2nd year at the Centre for Advanced Materials Processing and Manufacturing (AMPAM) at UQ where the student will learn about metal-based additive manufacture and produce prototypes for evaluation using suitable additive manufacturing techniques. Returning to Exeter to complete their PhD, the student will work on automated optimisation using the digital twin model, and publish their work in a series of journal and conference papers.

How to Apply:

https://www.exeter.ac.uk/studying/funding/award/?id=3932

 

University of Queensland projects

Supervisors:

Professor Neil McIntyre, Centre for Water in the Minerals Industry and Sustainable Minerals Institute, University of Queensland

Professor Slobodan Djordjevic, Professor of Hydraulic Engineering and co-director of the Centre for Water Systems, University of Exeter

Project Description:

Globally, water stress is driving the replacement or augmentation of traditional freshwater supplies with desalinated seawater supplies. The unlimited supply of water provided by the sea, the relatively high population living in coastal regions, and prospects of cleaner and cheaper energy to drive the supply system are among the factors driving this trend. North Chile is perhaps the best example, with around 20 desalination plants recently constructed or in the pipeline. Globally, major cities, traditionally with abundant freshwater supplies, such as Sydney, Melbourne and London have invested in seawater supply to as a security against global climate change and population growth.

However, the contribution of seawater supplies to wider sustainability dimensions is poorly understood. One important dimension is small‐medium scale irrigated agriculture, since this traditional industry is central to many societies globally and contributes considerably to local and regional food security, yet it often lacks climate resilience. Irrigated crops rarely have the economic value that would permit unsubsidised purchase of seawater. This holds even under the most optimistic projections of costs and revenues. Nevertheless, water sharing schemes may be possible that provide sustainability benefits for agricultural and all other water users.

Research is needed to improve understanding of: the infrastructure and operational rules that would maximise value from an integrated and shared water supply system; the resilience to climate and other variabilities and uncertainties; and the trade‐offs necessary between the system’s multiple objectives. At present, although generic water supply cost‐benefit frameworks and models exist, none have been adapted and applied to exploring the role of seawater supplies in small‐to‐medium scale sustainable agriculture.

This proposed project will initially use the case study of the Atacama desert in Chile to explore the future role of integrated seawater and freshwater supply networks in arid regions. The project will focus on understanding the system, model development, optimisation and scenario analysis, working as part of a team of hydrologists, engineers, economists and social scientists investigating broader questions around arid region seawater supply. This case study is chosen as the one for which we already have rich data sets and where the described problems are of high importance.

The second case study area will be a semi‐arid region in north‐east of Brazil, where there is a great potential for solar‐energy powered desalination plants, which will bring a further sustainability dimension to this project.

How to Apply:

https://graduate-school.uq.edu.au/quex

 

Supervisors:

Associate Professor Idriss Blakey, Australian Institute for Bioengineering and Nanotechnology, University of Queensland

Professor Nick Stone, Professor of Biomedical Imaging and Biosensing, University of Exeter

Project Description:

Chronic wounds (CW), wounds that persist more than 3 months, have been referred to as a major and snowballing public health burden which adversely affects the quality of life. In the US, 25 billion USD is spent annually on treatment of CW and the burden is growing rapidly due to increasing health care costs, an aging population and a sharp rise in the incidence of diabetes and obesity worldwide. CWs comprise of diabetic foot ulcers, pressure ulcers (bedsores), or burns and skin ulcers. Recent advances
in sericin hydrogel bandages aid in CW healing, but invariably restricts the assessment of the wound without removing the bandage. Often Reactive oxygen species (ROS) like oxygen, such as superoxide anion, hydroxyl radicals, singlet oxygen, and hydrogen peroxide, have been hypothesized to be increased (~170%) at chronic wound sites and delay healing. However, at lower levels ROS is expected to be beneficial, indeed wound treatments based on ROS modulation have shown promise provided that ROS levels are carefully controlled and appropriately timed. Thus, detecting and monitoring ROS
non-invasively (without removing the bandage), would enable ROS modulation therapy to be applied to optimise healing improving patient outcomes and minimising healthcare costs. Thus, the proposed project will explore the development of bandages that can detect ROS from wound site (sweat, pus) directly using a specialized Raman spectroscopy technique enabling us to see through the bandage and quantify ROS species and enable early identification of infections.

These externally wearable nano-hydrogel bandages will be made of polymer or sericin with embedded plasmonic nanostructures carrying the “reporter molecules”. The ROS from the wound site, once absorbed by the hydrogel will be transported to the embedded gold or silver nanostructures where they will chemically interact with the “reporter molecules”. These custom-made reporter molecules will then undergo specific reactions with ROS to give a chemical spectral fingerprint that is detectable using optical vibrational Raman spectroscopy. A suitable specialized hand-held Raman device working on the principles of surface enhanced spatially offset Raman spectroscopy (SESORS) (already built inhouse (Stone, Exeter) will allow sensitive detection from the bandage within a few seconds to
minutes, depending on the need to measure small or large areas. Moreover, the change in chemical signature (due to ROS interaction with reporters) will provide us with the ability to specifically and sensitively determine the concentration of ROS in the wound environment over time (initially to be done with spiked sweat samples) and correlate it to wound healing. This can lead to personalized treatment for the patients and promote healthy living.

This project builds upon work undertaken at Exeter developing SESORS and nanostructures for disease detection and UQ in the development of gold nanostructures modified with Raman reporter molecules for the detection of ROS.

How to Apply:

https://graduate-school.uq.edu.au/quex

 

Supervisors:

Dr Anne Cleary, Institute for Social Science Research, University of Queensland

Dr Ben Wheeler, Senior Lecturer at the European Centre for Environment and Human Health, University of Exeter

Project Description:

Climate change is the greatest threat to global health in the 21st century. While certain physical health impacts of climate change are well understood, for example heat-related morbidity and mortality, the acute and chronic mental health impacts of climate change remain poorly understood. Depression is already the single largest contributor to global disability. Failing to adequately consider the climate change implications on mental health could further exacerbate and accelerate growing global trends in mental ill-health.

Climate change compounds existing health inequalities with society’s vulnerable communities most at risk to climate change’s impacts on mental health. Ensuring healthy lives and wellbeing for all requires an understanding of these inequitable impacts of climate change on the mental health of marginalized and vulnerable populations.

The many varied links between climate change and mental health, which are highly socially and culturally mediated, raise challenges in the understanding, operationalisation and measurement of these complex relationships. There is an urgent need for exploratory research to assess potential links between climate change and mental health; and to progress the field through proposing evidence-informed scales and measurements of mental health impacts in the face of climate change. Current discussions on appropriate climate change related mental health indicators are deficit focused, with suicide rates proposed as the potential indicator. To adequately assess the impact of climate change on mental health, a more comprehensive conceptualisation of mental health and imagining of appropriate measurement tools is required.

This project will use existing data sources (such as national surveys) to explore trends in climate change related mental health illnesses, inequalities and resiliencies across geography, class and wealth. Population data from Australia and the United Kingdom will be explored with particular focus given to data representing communities vulnerable in both environmental and health terms, such as Indigenous, rural, remote and socio-economically deprived communities and young people. This exploration will shed light on the links between climate change and mental health and provide insight on practical and appropriate indicators for adequately assessing this relationship.

How to Apply:

https://graduate-school.uq.edu.au/quex

 

Supervisors:

Dr Jan Engelstaedter, School of Biological Sciences, University of Queensland

Dr Ben Longdon, Sir Henry Dale Wellcome Trust and Royal Society Research Fellow, University of Exeter

Project Description:

Pathogens are an inevitable part of every ecosystem. In humans as well as in livestock and natural systems, the majority of pathogens have only arrived in their host species relatively recently, by switching from a different host species. In addition to the epidemiological processes of within- and between-host species transmission, pathogens also often evolve very rapidly – their genomes change as they adapt to their host through processes such as mutation, natural selection and random genetic drift. To complicate things further, their hosts also undergo evolutionary change, and evolution in hosts and pathogens can be tightly linked: hosts evolve resistance to pathogens and in turn pathogens evolve to overcome this resistance, resulting in continuous coevolutionary arms races.

Evolutionary biologists have long been fascinated by host-pathogen coevolution and a large body of literature is devoted to understanding the dynamics, patterns of resistance and susceptibility arising through time and space, as well as the underlying infection genetics. However, the scope of these studies is almost exclusively restricted to a single pair of host and pathogen species.

In this project we will combine theoretical and experimental approaches to investigate more realistic systems in which coevolution between multiple pathogen and multiple host species are considered. For the theoretical part, to be conducted at The University of Queensland under the supervision of Dr Jan Engelstaedter, mathematical models will be constructed and analysed in which host species may interact with each other through various processes (e.g., competition or predation), and pathogens may switch more or less rapidly between host species. Both analytical methods and computer simulations will be used to assess how coevolution impacts the ability of pathogens to undergo host shifts and how in turn host shifts affect coevolution. In the experimental part of the project, to be performed at the University of Exeter under Dr Ben Longdon’s supervision, these theoretical predictions will
be scrutinised by using a system of bacteria as hosts (several Staphylococcus species) and
a range of viruses (bacteriophages) as their pathogens. This system is ideally suited to study host shifts in a high-throughput manner, and the experiments will be the first to interrogate the interplay between coevolution and the ability of pathogens to jump between host species. The results from this project are expected to have wide-ranging implication in
several fields, including ecosystem stability, emerging infectious diseases, and agriculture.

How to Apply:

https://graduate-school.uq.edu.au/quex

 

Supervisors:

Dr Matthew Mason, School of Civil Engineering, University of Queensland

Dr David Stephenson, Chair in the Statistical Modelling of Weather, University of Exeter

Project Description:

Severe thunderstorms are responsible for billions of dollars in damage to buildings, critical infrastructure and agricultural crops every year. Over the last decade, in Australia alone, there have been eight thunderstorm events that resulted in insured losses greater than half a billion dollars. The 2016 state-wide black out in South Australia was also triggered by severe thunderstorm winds that caused a number of high-voltage transmission lines to fail. Despite the clear and repeated impact of these events, limited tools exist for those exposed to this hazard (e.g., infrastructure operators, farmers) to sustainably manage or mitigate their risk. A lack of understanding about how severe thunderstorm activity will be influenced by climate change makes the sustainable management of this future risk even more complicated.

The aim of this project is to improve how severe thunderstorm risk is managed and mitigated. This will be achieved through the development of a stochastic, event-based thunderstorm hazard model that can be used to develop a severe thunderstorm climatology for a given region in the current climate and also determine how this climate will change into the future. This research will improve our understanding of severe thunderstorm activity and its drivers. It will also provide a decision support tool for policy makers, disaster managers and re/insurers who seek to better understand their exposure to severe thunderstorms and create innovative management solutions.

The stochastic thunderstorm hazard model will be developed using eastern Australia as a case study region, but the approach can be readily applied to other regions of the world. The general tasks required for completing the project include:

  1. Identify the broad scale environmental conditions that lead to severe thunderstorm observations (primarily wind and hail) on the ground. This will require the coupling and analysis of surface weather, radar, satellite and global reanalysis databases.
  2. Using global reanalysis data, determine the frequency of these thunderstorm conducive environments in the case study region.
  3. Based on 2, develop a stochastic model of thunderstorm occurrence in the case study region. It will be important within this task to ensure the spatial correlation between thunderstorm events is maintained so the broad scale features that trigger them, e.g. frontal systems, are effectively modelled.
  4. Repeat tasks 1-3 using historic data periods from within climate projection model outputs, then apply this new stochastic model to several future time periods and probabilistically quantify the expected change in thunderstorm activity.

How to Apply:

https://graduate-school.uq.edu.au/quex

 

Supervisors:

Dr Marina Fortes, School of Chemistry and Molecular Biosciences, University of Queensland

Dr Barbara Tschirren, Senior Lecturer in Evolutionary Ecology, University of Exeter

Project Description:

Some individuals age faster than others, experience age-related diseases earlier, such as osteoporosis, and ultimately live a shorter life. Understanding the biological basis of individual variation in ageing trajectories remains one of the big challenges for science. Genomic constraints and resource-based trade-off between reproductive investment and healthy ageing probably underlie individual susceptibility to age-related decline. However, these hypotheses remain experimentally untested.

We propose to use a unique avian life history model explicitly created to study the links between reproduction and ageing to address these knowledge gaps. Using an artificial selection approach, Dr Tschirren created replicated genetic lines of quails (Coturnix japonica) that differ in their ageing trajectories. Birds selected for high reproductive investment age quickly, suffer from osteoporosis symptoms and have difficulties to walk later in life, whereas birds selected for low reproductive investment age slowly and live longer. This experimental model is thus ideally suited to test the consequences of reproductive investment for the development of age-related disease such as osteoporosis. Genomes and transcriptomes of these quail will be scrutinized using systems biology methods to unravel the molecular basis of ageing processes, targeting the discovery of genes and pathways that affect reproduction, ageing and the development of osteoporosis.

This project combines the complementary skills and expertise of researchers in veterinary science, evolutionary biology, genomics and systems biology from the University of Queensland, Australia, the University of Exeter, United Kingdom and the Institute of Avian Research, Germany. This is a unique PhD research and training opportunity. In addition to available datasets, the student will conduct animal trials to test the potential of nutritional interventions in buffering genetic susceptibility to osteoporosis.

This cross-disciplinary and synergistic project will provide novel and essential insights into molecular constraints involved in the processes of reproduction that impact on healthy ageing and have broad implications for evolutionary biology, animal breeding and welfare, and human health. Animal breeders are interested in productive animals (i.e. highly fertile egg layers). However, the concern for animal welfare requires understanding of the trade-offs and the development of strategies that prevent diseases, such osteoporosis. The medical field is interested in understanding the genomics of age-related diseases as a step towards promoting healthy aging.

How to Apply:

https://graduate-school.uq.edu.au/quex

 

Supervisors:

Dr Ash Rahimi, Lecturer in Computer Science, University of Queensland

Dr Rudy Arthur, Lecturer in Data Science, University of Exeter

Project Description:

People don’t live in a flat society, they live in overlapping and interconnected communities sharing attributes such as geography, language, identity, and political opinion. Online communities mirror offline ones and are highly influential in determining how information is propagated and how opinion is shaped. The goal of this project is to utilise both linguistic clues and online social interactions to identify the social and linguistic structure of communities, their interactions and how these communities influence and shape opinions. Hidden factors that determine why users connect to each other on social media will be inferred and used to detect and measure communities, improving on traditional graph-based community detection methods.

The communication within and between these communities will be studied to understand the spread of cultural and linguistic innovation and how information and political ideas travel along these networks. For example, we are interested in studying the relationship between the core and the periphery, like London and the North of England or Sydney and Western Australia, answering questions like: How do the concerns of people in the periphery reach decision and policy makers in the core? How does culture like news, memes and hashtags spread around these networks? What biases do people in the core have about the periphery and vice-versa?

This will advance our knowledge on how information spreads and provide tools to magnify marginalised voices. The ideal student for this project will have both a keen interest in social and political issues as well as a strong technical background which they will build on in this project, becoming experts in NLP, network analysis and deep learning techniques.

How to Apply:

https://graduate-school.uq.edu.au/quex