Make your PhD extraordinary

Details

One fully funded, full-time PhD position to work with Dr Fengxiang He at the Artificial Intelligence and its Applications Institute (AIAI), School of Informatics, University of Edinburgh on a project titled “Machine Learning Theory & Economics”.

The aim of this project could be in deep learning theory, privacy in machine learning, theory of decentralised learning, symmetry in machine learning, learning theory in game-theoretical problems, and their applications in economics, such as auction, voting, resource allocation, and decentralised finance.

Candidate’s profile

·        A Bachelor’s Hons degree (at class 2.1 or above, or international equivalent) and/or Master’s degree in a relevant subject (mathematics, statistics, economics, or related subject).

·        A strong mathematical background, with an emphasis on analysis, algebra, geometry, differential equation, probability, and statistics. Recipients of mathematics competition medals are highly desirable.

·        Proficiency in English (both oral and written).

·        Relevant research experiences in machine learning, statistics, economics, etc. are desirable but not necessary.

·        Programming skills in Python, PyTorch, TensorFlow, etc. are a plus but not necessary.

Applicants should apply via the University’s admissions portal (EUCLID) and apply for the following programme: AIAI: Foundations and Applications of Artificial Intelligence, Automated Reasoning, Agents, Data Intensive Research

Applicants should select a start date to start their application, as follows:

-      1 May 2024 (Home applicants only)

-      1 September 2024 (Home and International applicants)

Application Information

Applicants should state “Machine Learning Theory & Economics” and the research supervisor (Dr Fengxiang He) in their application and Research Proposal document.

Complete applications submitted by 5 January 2024 will receive full consideration; after that date applications will be considered until the position is filled. The anticipated start date is 1 May 2024, 1 September 2024, or later depending on immigration requirements of the successful applicant.

Applicants must submit:

·   All degree transcripts and certificates (and certified translations if applicable).

·   Evidence of English Language capability (where applicable).

·   A short research proposal (max 2 pages).

·   A full CV and cover letter describing your background, suitability for the PhD, and research interests (max 2 pages).

·   Two references (note that it the applicant’s responsibility to ensure reference letters are received before the deadline).

Only complete applications (i.e., those that are not missing the above documentation) will progress forward to Academic Selectors for further consideration.

Contact

Applicants are highly encouraged to contact Dr Fengxiang He at F.He@ed.ac.uk to discuss their cases before submissions.

Environment

The School of Informatics is one of the largest in Europe and currently the top Informatics institute in the UK for research power, with 40% of its research outputs considered world-leading (top grade), and almost 50% considered top grade for societal impact. The University of Edinburgh is constantly ranked among the world’s top universities and is a highly international environment with several centres of excellence.

The School of Informatics is exceptionally strong in the area of AI, hosting one of the largest groups for AI in the world. The successful applicant will be part of the Trustworthy AI & Economics group and will have the opportunity to interact with the other members of the group and more widely within the School of Informatics.

Supervisor

Dr Fengxiang He is Lecturer at Artificial Intelligence and its Application Institute, School of Informatics, University of Edinburgh. He received BSc in statistics from University of Science and Technology of China, MPhil and PhD from the University of Sydney. His research interest is trustworthy AI, with emphasis on deep learning theory and explainability, theory of decentralised learning, symmetry in machine learning, learning theory in game-theoretical problems, and their applications in economics and finance. He is a member of IEEE's Global Initiative on XR Ethics, AI/ML Terminology and Data Formats Working Group, Decentralized Metaverse Initiative, and Ethical Assurance of Data-Driven Technologies for Mental Healthcare. He is an Area Chair of UAI, AISTATS, and ACML. Please visit https://fengxianghe.github.io/ for more information.

Apply

Dr Fengxiang He

Details

Obesity levels are rising rapidly around the world and are a major risk factor for diseases such as Type 2 diabetes, cancers and recently, COVID-19. New strategies and innovations are thus needed to prevent and treat obesity and associated disease. This requires an in-depth understanding of the genetic, cellular and physiological mechanisms underlying obesity.

Obesity is a systems-level disorder that affects multiple organs and perturbs inter-organ communication. A comprehensive understanding of obesity thus benefits from studies in simpler model organisms where this integrative network can be examined in vivo. With highly conserved signalling pathways and effective genetic and biochemical tools, the model system Drosophila melanogaster offers many advantages to study obesity and associated disorders.

In this PhD project, we will use genetic data from human obesity studies to generate specific Drosophila obesity models. Using these models, we will examine the role of candidate obesity genes in different organs and in inter-organ communication. We will focus on gaining new insights into the mechanistic role of candidate obesity genes in the adipose tissue and liver. Complex interactions between genetic and environmental factors that are known to influence obesity will also be assessed. This project will thus provide novel insights into the pathogenic mechanisms underlying obesity as well as the physiological processes that regulate metabolic homeostasis.

This project will give the student opportunities to learn and use the extensive repertoire of genetic tools available in Drosophila and a variety of methodological approaches including: confocal microscopy, biochemical and metabolic assays, immunohistochemistry, Drosophila nutritional and behavioural assays and molecular biology techniques.

Discussions with prospective students are welcomed and the final project will be designed incorporating the student’s interests.

https://www.ed.ac.uk/profile/neha-agrawal

Lab website: minchinlab.org

https://www.research.ed.ac.uk/portal/en/persons/james-minchin(284e253e-2cb1-412a-acad-8423df06637b).html

The School of Biological Sciences is committed to Equality & Diversity: https://www.ed.ac.uk/biology/equality-and-diversity

Details

Synopsis:

Trypanosome kinetoplast DNA is the most complex mitochondrial DNA network in nature and is a highly promising target for the development of novel drugs to treat trypanosomatid diseases. Using single-molecule imaging and genetic engineering methods, this project will investigate the structure and function of the trypanosome kinetoplast DNA in response to drugs and mutations.

Project description:

Trypanosomatid parasites cause devastating diseases in humans and their livestock, and these diseases predominantly affect low- and middle-income countries. A characteristic feature of these single-cellular organisms is their extraordinarily massive and complex mitochondrial DNA, also called kinetoplast DNA (kDNA).

Kinetoplast gene expression is critical for survival of these parasites, and interference with this process is an important part of the mode-of-action of some existing anti-trypanosomatid therapies. Inhibition of kinetoplast replication and expression is therefore an attractive target for the development of new therapies. In addition, the unique structure of the kinetoplast, which is composed of thousands of interlocked DNA rings, has attracted the interest of biophysicists as such ‘Olympic networks’ have unique material properties. However, the precise structure and biogenesis of the kinetoplast, and how these features relate to its function and physicochemical properties, have remained elusive.

By combining single-molecule imaging, sequencing, and genetic engineering, we are in a unique position to investigate kDNA biology in unprecedented detail. More specifically, by using mitochondrially targeted activator-like effector nucleases (mitoTALENS) we will perform precisely controlled structural kDNA manipulations. In turn, we will use atomic force microscopy (AFM) and optical tweezers to characterise kDNA structure and topology at single-molecule detail and in response of the mitoTALENS manipulations. This single-molecule approach will be complemented by deep sequencing and assembly tools, which will allow us to reveal the exact molecular composition of kDNA networks.

Specifically, this project will tackle the following questions:

- kDNA biogenesis is intimately linked to its replication and segregation mechanisms, which are largely mysterious. Using synchronised parasite cultures and specific probes for newly replicated molecules, the student will investigate the precise attachment sites and subsequent distribution of these molecules within the network during replication and dissect the process at single-molecule resolution.

- Some anti-trypanosomatid drugs kill parasites by inducing kDNA loss. However, the exact effects of these drugs on kDNA topology are unknown. The student will resolve this question by treating parasites with physiologically relevant drug concentrations, followed by single-molecule characterisation of kDNA topology (via AFM or optical tweezers).

The School of Biological Sciences is committed to Equality & Diversity: https://www.ed.ac.uk/biology/equality-and-diversity

Details

Summary

This project will explore the formation and mitigation of (bio)films on the surface of microplastics in wastewater treatment.

Project background

Pollution of water resources by microplastics (MP) is a global environmental problem that can cause toxic and adverse effects on human health and the environment. Wastewater treatment plants (WWTP) could be potentially one of the major sources of MPs release to the environment. For example, microfibres from clothing washing are transported in the raw effluent to WWTP where, because of their small size, they may bypass the treatment process. Furthermore, conventional biological processes cannot effectively destroy persistent and toxic organic compounds, such as MPs. Interactions of chemical and biological pollutants with MPs, which will enable the quantification of impacts and eco-toxicity of MPs, are not well understood yet.

Key research questions

• What are the sorption rates of organic (e.g. antibiotics, personal care products) and biological (Escherichia coli bacteria) pollutants, commonly found in wastewater, onto MPs?
• Which factors can affect pollutants sorption on MP and in what ways?
• Can wastewater treatment technologies alter the characteristics of (bio)film-coated MPs?

Methodology

The following experimental tasks will take place to address the project’s research questions:

Task 1. (0-18 months)

Lab-scale experiments will be carried out to estimate the adsorption rates of organic and biological pollutants, commonly found in wastewater, on the surface of weathered and pristine MPs. Representative pollutants will be selected and spiked in synthetic wastewater sample.

Task 2. (12-24 months)

Lab-scale experiments will be carried out to study the effect of factors that can affect the process. For example, the water matrix, the type and concentration of MPs, ambient temperature and light, processes that are typically used in wastewater treatment (e.g. UV).

Task 3. (12-36 months)

Analytical and microscopy methods will be applied to monitor changes on the surface of MPs and on (bio)film formation. Detection of MP degradation products in water will be also monitored. Monitoring methods will include scanning electron (SEM) and atomic force microscopy (AFM). Moreover, gas chromatography (GC-MS) and liquid chromatography (HPLC) will be used for quantification of organic compounds in water samples.

Apply by Thu Jan 04 2024 at 12:00 via https://www.ed.ac.uk/e4-dtp/how-to-apply/supervisor-led-projects/project...

Further Information: 

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Details

Antibiotics have been widely used in improving human and livestock health as well as preventing bacterial infection (Gan et al., 2018). After use, most of the unmetabolized antibiotics will be excreted via feces and urines. The hydrophilic nature and undesirable removal efficiency of antibiotics by traditional wastewater treatment technologies resulted in large amounts of their residuals being discharged into environment, which have been widely detected in various waters (e.g., river, lake and groundwater) (Luo et al., 2019). This has attracted global concern due to their effects of antimicrobial resistance (AMR) as well as their acute or chronic toxicity, and these chemicals together with other micropollutants (e.g., PFAS and neonicotinoids etc.) are well known as emerging contaminants (ECs) (Ma et al., 2020a, 2023; Zhang et al., 2023).

Various technologies including advanced oxidation processes (AOPs) (e.g., photocatalytic, activated persulfate (PS) and Fenton systems), adsorption, membrane filtration and biodegradation have been studied to eliminate antibiotics from waters (Chen et al., 2023). AOPs was proved to be a promising approach as it was able to completely remove the target contaminants by oxidizing them into non-toxic (e.g., CO2 and H2O) or less toxic transformed products (TPs). Specially, activated PS technology has aroused broad attention due to its higher redox potential (E0=2.6-3.1 V) and longer half-life (28-40 μs) of SO4•- derived from PS activation (Liu et al., 2023). While the exogenous addition of PS would increase the operational cost and cause secondary pollution. Recent studies suggested that PS could be in-situ electrogenerated at the anode in the solution containing sulphate, which offers the possibility without adding exogenous PS for this technology (Chen et al., 2022). Sulphate is frequently distributed in wastewater and its concentration could reach up to 1000 mg/L, which provides potential precursors for PS in-situ generation by electrochemical oxidation (EO-PS) process (Chen et al., 2022).

Generally, PS has low reactivity toward pollutants, and it is essential to activate persulfate to produce oxidizing SO4•- and •OH (Bai et al., 2021). Carbon-based materials (e.g., biochar) were considered as promising PS activators, which can be synthesized by a variety of feedstocks, such as wheat straw, maize cobs, soya bean dregs, municipal sludge (MS) (Xu et al., 2021) and so on. Comparatively, MS is considered to be a superior feedstock for its high organic matter content, large specific surface area, large pore volume and stability structure (Li et al., 2021). Moreover, MS is a main by-product of municipal wastewater treatment plants and the carbonation of sludge (e.g., sludge biochar (SBC)) favors the immobilization of carbon which can address to dispose the wastes (e.g., sludge) properly (Ma et al., 2020b).

The catalytic activity of biochar is largely dependent on the available sites on their internal or external surface, and the pristine biochar with limited functional groups are usually not ideal for PS activation (Li et al., 2022). Different methods (e.g., N-doping, ball milling and transition metals loading) have been developed to enhance the functionality of biochars for boosting their activation performance for PS (Ma et al., 2023). Additionally, ball milling can break biochar into ultrafine particles to increase its specific surface area (SSA), introduce more oxygen-containing functional groups and develop more porous and defective structures (Qu et al., 2022), which is beneficial for the biochar activation performance with simple operation (Ma et al., 2023) while it has not been much applied for oxidants activation (Tang et al., 2023).

Therefore, this project will aim to develop a sustainable removal technology for emerging contaminants from waters. A major objective of the project is to develop and optimize an in-situ generation technology of EO-PS for ECs (e.g., initially antibiotics and then other ECs such as PFAS and neonicotinoids etc.) removal, including elucidation of the detailed mechanism for PS production. A second key objective is to synthesize functionalized biochar from waste materials (e.g., municipal sludge) to efficiently activate EO-PS. Different modification methods (e.g., N-doping, ball milling and transition metals loading) will be tested for the functionalized biochars synthesis for their activation efficiency. A final objective is to couple in-situ electrogenerated PS with carbon-based material for contaminants (e.g., starting with antibiotics) degradation, including the extension to other ECs elimination and testing for real water treatment.

Applicants are strongly advised to make an informal enquiry about the PhD to the primary supervisor well before the final submission deadline.

Applicants must send a completed Hydro Nation Scholarship application form and their Curriculum Vitae to Dr Zulin Zhang - zulin.zhang@hutton.ac.uk by the final submission deadline of 10^th January 2024.

Visit website

Details

Summary

Disaster risk weakens climate adaptation. This PhD will explore the DDR-CCA nexus by assessing the vulnerability of adaptation assets to multi-hazards; and examining the transformative potential of risk-informed development.

Project background

Climate risk is outstripping adaptation action, creating a widening gap. This gap is worse in Majority World geographies that experience interacting geophysical (e.g., earthquakes) and climate (e.g., flooding) hazards exacerbated by structural, social and economic inequalities. Consequently, many adaptation gains are at risk of being lost by these overlapping rapid onset geo-climate hazards. Despite recent progress, the connections within and between policy and practice in DRR and CCA remain weak – encouraging unsustainable development planning practices and maladaptation. Moreover, DRR and CCA policy is often developed in isolation, with risk assessments and planning tools not conventionally co-developed or aligned. Additionally, limitations in research and practice are imperiling ‘hard’ and ‘soft’ adaptation measures in place e.g., the development of non-earthquake resilient cyclone shelters or green energy technologies located in areas of land instability or on floodplains. Critically, the repercussions for Loss and Damage could be considerable but are currently unknown and under researched. We lack key information to drive policy-ready integrated multi-hazard climate risk assessments and planning, without which, a sustainable just and green transition is impossible. 

Adopting a place-based and comparative approach, this project will identify a range of adaptation actions which will then be stress-tested through a DRR prism. Utilising a multi spatial-temporal perspective, the project will examine both locally led adaptation (LLA) actions and nationally or regionally implemented built adaptation infrastructure. The student will examine the challenges, barriers, opportunities for operationalising risk-informed sustainable development at local and national levels according to 2030 and 2050 climate scenarios. They will design a method to stress test identified adaptation actions and design relevant risk scenarios that could be used as evidence for decision making. 

Research questions

1. What are the international and national policy gaps, overlaps and opportunities for harmonising DDR and CCA?
2. What are the spatial-temporal connections between maladaptation and lack of multi-hazard awareness, and what implications does this have for loss and damage?
3. To what extent can an LLA perspective support communities to achieve feasible adaptation pathways?
4. What approaches are needed to ensure a multi-hazard risk framing for adaptation pathways in Majority World geographies?
5. How can integrated climate risk scenarios be created for development planning?

Please see full description and details on how to apply in https://www.ed.ac.uk/e4-dtp/how-to-apply/supervisor-led-projects/project?item=1603

Details

There is no sustainable development without Disaster Risk Reduction (DRR) but progress towards international DRR objectives has been slow. This research is a critical analysis of science integration in decision making from international policy to local implementation.

Project background

The Sendai Framework for Disaster Risk Reduction (DRR) was a pivotal moment for DRR policy progression and for the acceptance and integration of science led thinking within policy through to practice. The scientific community led by the Science and Technology Advisory Group (STAG) lobbied successfully to ensure science was integrated within the policy. It was also pivotal for furthering the use of science in decision making at all levels. However, there have been many challenges too. The mid-term review of Sendai’s progress demonstrates that although progress has been made, clear evidence of the process and impact of science led or informed decision making for DRR is limited. 

Aitsi-Selmi (2016) suggested key areas of focus for future science uptake for DRR through the Sendai framework but few studies have examined whether these areas have been addressed or successful.

Project detail

This project will identify and address the challenge of science integration for Disaster Risk Reduction at policy and critically, implementation scales. Comparing two low-middle income country case studies, Indonesia and African nation (to be identified) this collaborative project with the British Geological Survey will explore the role of science within an established Disaster Risk Management national policy. Indonesia was one of the first countries to adopt a national to local level DRM policy after the devastating Boxing Day Tsunami. Forced to reconsider DRM the government was one of the first globally to achieve a tailored DRM policy with associated funding at the local level. Working alongside collaborative partners at the British Geological Survey and the Universitas Gadja Mada, the project will examine the current DRR international and national policy landscape, then develop a critical method to understand the challenges and barriers to science informed risk reduction through a series of case study led narratives. It is anticipated that this case study will then be compared to an African nation identified during the first 6 months of the project. The research will aim to develop improved policy ready mechanisms and recommendations for the successor of the Sendai Framework for DRR, beyond 2030 and science strategy.

Research questions

1. How has science been integrated within international agreements and national policy for DRR?
2. What is the perceived vs actual role of science for DRR?
3. What does science informed DRR look like on the ground and does it exist in reality?
4. Is there a mechanism for capturing or measuring science driven / informed DRR?
5. What is the future of science within DRR policy beyond 2023?

Please see E4 DTP webpage for full information and details on how to apply:

https://www.ed.ac.uk/e4-dtp/how-to-apply/supervisor-led-projects/project?item=1601

Details

Research environment: The programme is held by the University/British Heart Foundation Centre for Cardiovascular Science. Research is investigator-led and based around key themes: Cardiovascular Injury, Repair & Regeneration; Hypertension & Kidney Disease; Metabolism Obesity and Diabetes, Cardiovascular Data Science; Cardiometabolic Imaging. Based on the BioQuarter campus, research is supported by excellent core research facilities. Our training emphasizes translation and encourages integration of diverse skills in multidisciplinary research projects.

Programme Structure: The first year is a full Masters by Research, providing a core training of research and analytical skills over three projects each hosted by a different research group. This hands-on training is enhanced by Investigator-led tutorials highlighting recent advances in the literature. Workshops to develop transferrable skills, including critical reading, scientific presentation/writing and project planning, as well as research ethics are also a key component of the training. Taught and skills-based elements are credit bearing and innovative Student Led, Individually Created Courses (SLICCs) allow students to gain credits for their experiential training through interaction with other Centre activities such as public outreach, funding strategy, business development and epidemiological/societal impact of heart and circulatory disease.

PhD project: The experience gained during the first year enables each student to make an informed choice of PhD research area/supervisory team. Project matching is a student-driven process and takes place in May/June of the Masters year, supported by the Programme Director, Professor Matt Bailey. The PhD is undertaken from years 2-4 of the Programme and is a distinct University Award, requiring successful completion of the Masters programme. 

Candidates: you are an innovative and highly motivated scientist that views this programmes as the gateway to a career in cardiovascular research. You will have a BSc (Hons) degree (First or Upper Second class or equivalent) or a Masters qualification (Distinction or Merit) in the biomedical sciences, including (bio)informatics or in related disciplines such as computational science. Previous research experience is desirable. It is expected that you will be informed about our research and be able to articulate interest in specific research areas within our research themes.

We especially encourage applications from non-traditional backgrounds and those from under-represented groups. The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality.

A generous tax-free stipend, research costs and tuition fees (UK rate) are provided. Scholarships may be available competitively for those classed as International Students (EU and rest of the world). Please verify your fee status with the University's Registry before making your application and consider the potential for visa costs at the outset. Please note: If successful, International Students will need to apply for separate visas for the MScR and PhD programmes within UK government guidelines at the appropriate time and be able to cover National Health Service surcharges. These costs are not covered by the programme

Enquiries and Information: Please consult www.cvs.ed.ac.uk or contact Professor Matthew Bailey (Matthew.Bailey@ed.ac.uk) for further information.

Application: Please include a one-page personal statement highlighting your motivation for applying to Edinburgh and indicating areas of our research in which you are interested as well as your CV and two academic references. All documents should be sent or emailed to:

Lynn Meikle

Centre for Cardiovascular Science

The Queen’s Medical Research Institute

47 Little France Crescent,

Edinburgh, EH16 4TJ. Tel: +44(0) 131 242 6724

Email: Lynn.Meikle@ed.ac.uk

Closing date: January 15^th 2024

Interview date: February 2024

Details

The Advanced Care Research Centre (ACRC) is an interdisciplinary, £20M research centre at the University of Edinburgh. The ACRC will lead society’s response to the grand challenge of an ageing population that is growing in size, longevity and needs through the pursuit of research intended to deliver “high‐quality data‐driven, personalised and affordable care to support the independence, dignity and quality‐of‐life of people living in their own homes and in supported care environments”. 

This project sits within the ACRC Academy , a dedicated Centre for Doctoral Training, co-located with the ACRC, whose students will deliver key aspects of the ACRC research agenda through a doctoral-level research and training programme that will also equip them for careers across a wide range of pioneering and influential leadership roles in the public, private and third sectors. 

The PhD with Integrated Study in Advanced Care is a structured, thematic, cohort-based, programme of 48 months duration. Each PhD research project within the Academy has been devised by a supervisory team comprising academic staff from at least two of the three colleges within the University of Edinburgh. Each annual cohort of around twelve will include students with disciplinary backgrounds spanning from engineering and data science to humanities, social science, business, social work, medicine and related health and care professions. This unique level of interdisciplinarity is a key attribute of our programme. 

Project  

Aim

To understand why pain is associated with frailty using big data approaches including genetics, health record data, and longitudinal social and psychological survey data.

Objectives

• To isolate the biological pathways underlying the pain-frailty association by using genetic and epigenetic data

Proposed analysis includes multivariate analysis (e.g., genomic structural equation modelling) of genome-wide association study results of traits correlated with pain and frailty (e.g., depression) to understand how these traits overlap genetically with each other and with pain and frailty. Then common variants can be identified that are uniquely associated with both pain and frailty and these used in a biological pathway analysis to uncover relevant biological pathways. There is further scope to include epigenetic markers of pain and frailty into this analysis which will make it particularly novel.

• To use longitudinal pain data from childhood to predict frailty in later life (around 64 years)

Proposed analysis is in the 1958 British Birth Cohort where childhood and adult measures of pain are available. The most recent collection wave is soon due for completion with data accessible in autumn 2024. Participants will be aged between 62 and 64 years but a frailty index can nevertheless be computed, as was successfully done in this cohort when participants were in their 50s. Here we can test for longitudinal relationship between pain and frailty to accrue evidence of causal effects of pain (i.e., if childhood pain has a lasting association on frailty), and also test hypotheses where pain in adulthood is a mediator of causal effects like manual occupation and accidents/injuries.

• To compare medication and polypharmacy affects on frailty in those with and without pain

Proposed analysis will be in UK Biobank using the longitudinal data available at the imaging data wave and restricted to individuals older than 60 or 70 years (depending on sample size and thus power). UK Biobank has medical data linkage and we can use the medication data to look at medication type and polypharmacy effects on frailty progression in those with and without pain conditions (these data available from baseline).

• To understand what frail people with a history of chronic pain identify as their greatest challenges to good health through patient and public involvement.

This will either be interview based or focus group led work that will follow on nicely from our empirical studies. Here we are aiming to understand what the specific challenges might be for maintaining good health in people with a history of chronic pain. This could point to factors that exacerbate a person’s progression to frailty. It will be interesting to see whether any of the information aligns with mechanisms we uncover in our quantitative work. The student will work with the ACRC’s PPI panel to achieve a project that is meaningful to researchers and stakeholders.

Description

This project investigates why pain is a strong predictor of frailty. It will use a range of genetic, medical record linkage, and longitudinal survey data collected in the 1958 British Birth Cohort and UK Biobank, and genome-wide association study results of traits correlated with pain and frailty. If there is a causal relationship, then which biological pathways involved in pain increase frailty risk? If pain is a mediator, then what factors precede pain (e.g., occupation type)? And does medication modify frailty risk in those experiencing pain? Importantly, what do frail people with a history of pain identify as life challenges?

Eligibility

• Basic statistical analysis skills (multivariate statistics training is available within the Psychology Department)
• An interest in patient and public involvement in research, which might include interviews or focus groups
• Knowledge of genetics is desirable but not essential

We are specifically looking for applicants who will view their cutting-edge PhD research project in the context of the overall vision of the ACRC, who are keen to contribute to tackling a societal grand challenge and who can add unique value to – and derive great benefit from – training in a cohort comprising colleagues with a very diverse range of disciplines and backgrounds. We advise prospective candidates to engage in dialogue with the named project supervisor and/or the Director of the Academy prior to submitting an application. 

The Academy aims to foster a supportive and collaborative culture, and welcomes candidates with diverse backgrounds and experiences.

You must read How to apply prior to application

Please Apply here

Details

The Advanced Care Research Centre (ACRC) is an interdisciplinary, £20M research centre at the University of Edinburgh. The ACRC will lead society’s response to the grand challenge of an ageing population that is growing in size, longevity and needs through the pursuit of research intended to deliver “high‐quality data‐driven, personalised and affordable care to support the independence, dignity and quality‐of‐life of people living in their own homes and in supported care environments”. 

This project sits within the ACRC Academy , a dedicated Centre for Doctoral Training, co-located with the ACRC, whose students will deliver key aspects of the ACRC research agenda through a doctoral-level research and training programme that will also equip them for careers across a wide range of pioneering and influential leadership roles in the public, private and third sectors. 

The PhD with Integrated Study in Advanced Care is a structured, thematic, cohort-based, programme of 48 months duration. Each PhD research project within the Academy has been devised by a supervisory team comprising academic staff from at least two of the three colleges within the University of Edinburgh. Each annual cohort of around twelve will include students with disciplinary backgrounds spanning from engineering and data science to humanities, social science, business, social work, medicine and related health and care professions. This unique level of interdisciplinarity is a key attribute of our programme. 

Project  

Aim

Understand and implement co-design practices to optimsie smart AI and data driven health monitoring furnitures for different care applications.

Objectives

• Explore co-design methods/techniques for sensor and system designs
• Determine the co-design method that is suitable for the proposed technology.
• Validation of co-design development methods for initial prototype(s).

Description

This research programme will develop practical, care-driven, person centric technologies that are fit for people in later life and co-designed by people in later life. In particular, this means exploring and developing techniques to co-design existing unobtrusive sensors that have been created in WP 6 and optimizing its design and implementation based on the needs of end users. 

Eligibility

Candidates need to have a good degree in Electronic Engineering or Computer Science. Experience with sensor based systems and/or implementation of AI based embedded systems is desirable.

Prospective applicants could also have a good understanding of co-design methodologies, with a special interest in those with experience in incorporating co-design elements in developing technologies.

We are specifically looking for applicants who will view their cutting-edge PhD research project in the context of the overall vision of the ACRC, who are keen to contribute to tackling a societal grand challenge and who can add unique value to – and derive great benefit from – training in a cohort comprising colleagues with a very diverse range of disciplines and backgrounds. We advise prospective candidates to engage in dialogue with the named project supervisor and/or the Director of the Academy prior to submitting an application. 

The Academy aims to foster a supportive and collaborative culture, and welcomes candidates with diverse backgrounds and experiences.

You must read How to apply prior to application

Please Apply here