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Current vacancies

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PhD Opportunity: Remote patient monitoring using wearable devices and AI

About the Project

Artificial intelligence (AI) has permeated various aspects of modern life, significantly influencing the healthcare sector. Fuelled by this momentum, remote patient monitoring facilitated by mobile sensing technology is on the way to changing how patients are monitored and treated. By analysing extensive data from wearable devices and smartphones, you will build AI-based models that can lead to early detection of potential health issues and personalised interventions. The integration of AI and remote patient monitoring holds immense promise for improving healthcare outcomes. 

In this PhD project, you will conduct a comprehensive analysis of extensive datasets, such as RADAR-CNS and/or Mobilise-D, collected from large cohorts of individuals monitored remotely for over two years. These datasets encompass a diverse range of physiological and behavioural parameters, including heart rate, sleep patterns, physical activity, GPS location, and phone/app use. They represent the most extensive observational studies utilising wearable devices to date.

Your primary tasks will include: 1) to extract meaningful features from these sensor data and apply machine learning algorithms to predict health outcomes; 2) to explore advanced deep learning methodologies to further exploit the information embedded within the data, with the goal of improving prediction accuracy. The targeted medical conditions for analysis may include depression, multiple sclerosis, and Parkinson’ s disease.

For informal discussion about the project, please contact Dr. Shaoxiong Sun, shaoxiong.sun@sheffield.ac.uk. Please add quote [PHD-AI4Health] in the email subject line.

Supervisor Bio

Dr. Shaoxiong Sun is a Lecturer in Pervasive Data Science at the Department of Computer Science, the University of 91̽��. Previously, he was a Senior Research Associate in Data Science in Mobile Health at King’s College London. Dr. Sun's research interests primarily revolve around physiological and behavioural monitoring, leveraging advanced signal processing and machine learning methodologies. Dr. Sun has over 30 peer-reviewed publications and 2 patents granted through the PCT system.

About the Department & Research Group

The role will be based at the Department of Computer Science, the University of 91̽��. 99 percent of our research is rated in the highest two categories in the REF 2021, meaning it is classed as world-leading or internationally excellent. We are rated as 8th nationally for the quality of our research environment, showing that the Department of Computer Science is a vibrant and progressive place to undertake research. The successful candidate will join the Pervasive Computing research group. Its research focuses on the utilisation of mobile or pervasive sensing techniques to support faster and safer preventive care, improved patient-centred practice, and enhanced sustainability. 

Candidate Requirements

Minimum 2.1 Bachelor’s degree or Master’s degree in a relevant discipline (e.g., Computer Science, Electrical Engineering, Biomedical Engineering, and Bioinformatics), or its international equivalent.

Self-motivated and passionate about conducting research in artificial intelligence (AI) and its applications in healthcare innovation.

Proficient in data analytics, machine learning and signal processing (time series analysis), with hands-on experience in applying these techniques to real-world datasets.

Strong programming skills in Python and/or MATLAB.

Demonstrated experience in preparing scientific manuscripts for journals or conferences.

How to Apply

To apply for a PhD studentship, applications must be made directly to the University of 91̽�� using the Postgraduate Online Application Form. Make sure you name Dr. Shaoxiong Sun as your proposed supervisor.

Information on what documents are required and a link to the application form can be found here -

The form has comprehensive instructions for you to follow, and pop-up help is available.

Funding Notes

This PhD studentship will cover standard UK home tuition fees and provide a tax-free stipend at the standard UK Research Council rate (currently £19,237 for 2024/25) for 3.5 years. If you are an overseas student, you are eligible to apply but you must have the means to pay the difference between the UK and overseas tuition fees by securing additional funding or self-funding. Further information on International fees can be found here .

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How does non-coding RNA structure regulate gene expression?

About the Project

Background:

Long non-coding RNAs (lncRNAs) play a vital role in human physiology through regulating gene expression, impacting fundamental cellular processes. Dysregulation and mutations in lncRNAs are associated with cancer formation, neurodegeneration and cardiovascular disease. However we still do not fully understand how they function and cause these diseases.

lncRNAs fold into complex and dynamic structures, but the structural basis for their function remains poorly understood. In this project you will use cutting-edge single-molecule approaches to measure the structure of individual RNA molecules. We will use the HOTAIR lncRNA as a model (which has a well characterised secondary structure and defined binding partners), and deconvolute distinct conformational states to investigate the role of RNA structures in lncRNA function. 

Objectives:

1. Use single-molecule optical-tweezers and AFM to determine the structural conformations of the HOTAIR lncRNA 
2. Generate and characterise recombinant chromatin remodelling proteins known to interact with HOTAIR
3. Investigate how HOTAIR RNA structure effects the interaction and activity of these proteins 

Approach:

Conventional methods of studying lncRNA structure only report an average of the many possible conformations. Using single-molecule approaches of optical-tweezer and AFM we will identify and characterise specific conformational states. 

The full HOTAIR lncRNA, and individual domains, will be tethered in optical-tweezers assays allowing sub-pN force manipulation and measurements. This will allow us unprecedented insight into the conformation and stability of the RNA secondary structure. Using the complementary approach of high-resolution AFM we will obtain structures of individual molecules. 

HOTAIR is known to interact with the chromatin modifying protein complexes to regulate gene expression. These will be generated recombinantly and their interactions and activity with HOTAIR characterised using biochemical and single-molecule biophysics assays. Together this will enable us to understand the structural basis of HOTAIR lncRNA activity in regulation of gene expression through interaction with chromatin modifying enzymes. The project will provide exciting and novel insights into the mechanism of lncRNAs.

Apply:

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application. For any queries about the project please contact Dr Matt Newton matthew.newton@sheffield.ac.uk.

About YBDTP:

YBDTP brings together world-class bioscience research and innovation, as well as excellence in doctoral supervision, across the region. YBDTP will fund postgraduate researchers at the Universities of Leeds, 91̽��, York, Bradford, Hull, Leeds Beckett, 91̽�� Hallam and Teesside forming a strong regional training partnership. In YBDTP you'll benefit from a regional doctoral training programme that has interdisciplinary collaboration at its core. The aim is to enable you to develop a range of research skills in biological, biotechnology and biochemical areas as well as equip you with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers.

Funding Notes:

Funding: A tax-free annual stipend at the standard UKRI rate (£19,237 for 2024/25 entry), research costs and tuition fees at UK rate

Academic year: 2025/26

Open to: International (including EU) and UK (home) students

Eligibility:

Open to International (including EU) and UK (home) students.

You can apply if you have, or are expecting to gain, at least an upper second-class honours degree or equivalent. Due to the interdisciplinary nature of this programme, we welcome applications if your background is in any biological, chemical or physical science or mathematics and are interested in using your skills in addressing biological questions. International students should check if they meet the .

We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international (EU and non-EU) applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme. 

The Yorkshire Bioscience DTP is committed to recruiting extraordinary future scientists regardless of age, ethnicity, gender, gender identity, disability, sexual orientation or career pathway to date. We understand that commitment and excellence can be shown in many ways and have built our recruitment process to reflect this. We welcome applicants from all backgrounds, particularly those underrepresented in science, who have curiosity, creativity and a drive to learn new skills.

English language:

If English isn't your first language, you may need to provide evidence of your English language ability.

How to apply:

Application deadline: Monday 6th January 2025

To submit your application, click on the link to the Expression of Interest form in the Funding Notes section of any of your chosen projects. You can apply for up to 3 YBDTP projects (which can be at different universities).

We would advise you to read the questions in the form before submitting your application. Inside the form there is a link to a document for you to see the questions in advance.

If you have any questions about the application process, please email wrdtp@leeds.ac.uk.

If you have any questions about the project you are interested in, please email the project supervisor (details inside the project description).

How we allocate:

Shortlisting will take place as soon as possible after the closing date and successful applicants will be notified promptly. If you're shortlisted, you'll be invited for an interview on a date to be confirmed in February 2025. You'll be notified as soon as possible after the interview dates whether your application has been successful, placed on a reserve list or unsuccessful. If you are successful, you'll be required to confirm your intention to accept the studentship within 10 days.

Terms and conditions:

The studentships are fully funded for four years, and you must complete your PhD in four years.

You'll receive the UKRI minimum doctoral stipend per year for your living costs, which is paid to you in regular instalments. The  (UKRI) stipend is tax free and does not need to be paid back.

International students will need to have sufficient funds to cover the costs of their , , travel insurance and travel to the UK as these are excluded from UKRI funding.

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Funding Notes

To apply for a studentship from the Yorkshire Bioscience Doctoral Training Partnership to undertake this project, please complete an expression of interest form for October 2025 recruitment here: 

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PhD Opportunity:  The role of keratin networks in maintaining epithelial tissue integrity

About the Project

Epithelial tissues serve as protective barriers that line the organs and tissues of the body, ensuring integrity during mechanical stresses. Epithelial tissues are characterised by the expression of keratin proteins, which assemble into flexible, unbranched filaments to create complex cytoplasmic networks. These networks span across multiple cells, providing structural rigidity to the tissue. Keratins are less studied compared to other cytoskeletal filaments and their role in dictating the material properties of tissues and response to forces in proliferative tissues remains poorly understood.

This 4-year PhD project aims to map keratin network architecture in epithelial monolayers and understand how they are reorganised in response to strain when cells divide within the tissue or when tissues are stretched. You will use state-of-the-art live-cell super resolution microscopy and atomic force microscopy to both visualise the 3D architecture of keratin networks and map the mechanical landscape of the cell surface. You will explore how these networks influence mechanical properties across scales, and investigate their response to both local and tissue-scale forces.

This highly interdisciplinary project would suit a student with a background in either biophysics or biosciences, who is keen to develop skills in microscopy, quantitative image analysis and biophysical techniques. You will work together with a dynamic and friendly team of researchers across three different laboratories including cell biologists, biophysicists, engineers and computational experts. This project will give you the opportunity to develop cutting-edge imaging and analytical tools to answer fundamental biological questions about how tissues maintain integrity in the face of mechanical stress.

For informal enquiries about the project, please contact Dr Helen Matthews h.k.matthews@sheffield.ac.uk.

About YBDTP:

YBDTP brings together world-class bioscience research and innovation, as well as excellence in doctoral supervision, across the region. YBDTP will fund postgraduate researchers at the Universities of Leeds, 91̽��, York, Bradford, Hull, Leeds Beckett, 91̽�� Hallam and Teesside forming a strong regional training partnership. In YBDTP you'll benefit from a regional doctoral training programme that has interdisciplinary collaboration at its core. The aim is to enable you to develop a range of research skills in biological, biotechnology and biochemical areas as well as equip you with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers.

Funding Notes:

Funding: A tax-free annual stipend at the standard UKRI rate (£19,237 for 2024/25 entry), research costs and tuition fees at UK rate

Academic year: 2025/26

Open to: International (including EU) and UK (home) students

Eligibility:

Open to International (including EU) and UK (home) students.

You can apply if you have, or are expecting to gain, at least an upper second-class honours degree or equivalent. Due to the interdisciplinary nature of this programme, we welcome applications if your background is in any biological, chemical or physical science or mathematics and are interested in using your skills in addressing biological questions. International students should check if they meet the .

We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international (EU and non-EU) applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme. 

The Yorkshire Bioscience DTP is committed to recruiting extraordinary future scientists regardless of age, ethnicity, gender, gender identity, disability, sexual orientation or career pathway to date. We understand that commitment and excellence can be shown in many ways and have built our recruitment process to reflect this. We welcome applicants from all backgrounds, particularly those underrepresented in science, who have curiosity, creativity and a drive to learn new skills.

Not all projects will be funded; a limited number of candidates will be appointed via a competitive process.

English language:

If English isn't your first language, you may need to provide evidence of your English language ability.

How to apply:

Application deadline: Monday 6th January 2025

To submit your application, click on the link to the Expression of Interest form in the Funding Notes section of any of your chosen projects. You can apply for up to 3 YBDTP projects (which can be at different universities).

We would advise you to read the questions in the form before submitting your application. Inside the form there is a link to a document for you to see the questions in advance.

If you have any questions about the application process, please email wrdtp@leeds.ac.uk.

If you have any questions about the project you are interested in, please email the project supervisor (details inside the project description).

How we allocate:

Shortlisting will take place as soon as possible after the closing date and successful applicants will be notified promptly. If you're shortlisted, you'll be invited for an interview on a date to be confirmed in February 2025. You'll be notified as soon as possible after the interview dates whether your application has been successful, placed on a reserve list or unsuccessful. If you are successful, you'll be required to confirm your intention to accept the studentship within 10 days.

Terms and conditions:

The studentships are fully funded for four years, and you must complete your PhD in four years.

You'll receive the UKRI minimum doctoral stipend per year for your living costs, which is paid to you in regular instalments. The  (UKRI) stipend is tax free and does not need to be paid back.

International students will need to have sufficient funds to cover the costs of their , , travel insurance and travel to the UK as these are excluded from UKRI funding.

Funding Notes

To apply for a studentship from the Yorkshire Bioscience Doctoral Training Partnership to undertake this project, please complete an expression of interest form for October 2025 recruitment here: 

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PhD Opportunity: Developing Imaging Solutions to Study the Dynamics of Human Condensin I

About the Project

When cells divide, the genome is packaged into chromosome by the condensin I and II complexes. This process is fundamental; mutations or perturbations of condensin complexes and their regulators cause developmental disorders and are linked to cancer.

Condensin complexes work by extruding loops of DNA, and we can directly visualise this activity using single-molecule imaging of DNA with Total Internal Reflection Fluorescence (TIRF) microscopy. While this approach gives us a way of visualising activity, to gain insights into how condensin complexes work, we must couple this information with mechanical movements of the complex.

Foster resonance energy transfer (FRET) is a method to detect the proximity of a donor and acceptor fluorophore, allow conformational changes of a single-molecule to be visualised over time using fluorescence. The aim of this project is to develop an optimised TIRF system to deconvolute fluorescence wavelengths, enabling easier measures of condensin conformational changes and colocalization.

The aims of project are:

1. Develop an optimised TIRF microscope.

2. Image and analyse recombinant condensin samples.

3. Iteratively improvement in imaging system.

You will work with a supervisory team composed of Dr. Erin Cutts, Prof. Ashley Cadby and Dr. Matthew Newton, bringing together expertise in condensin complex biology, protein biophysics and microscopy development.

91̽�� University hosts flagship institute focused on nucleic acids and single-molecule imaging, providing opportunities for additional support, collaboration and inspiration. We encourage our students to attend and present at national and international meetings and training events, and support them in their professional development.

For further information check out our lab web page  or contact us (e.cutts@sheffield.ac.uk). 

About YBDTP:

YBDTP brings together world-class bioscience research and innovation, as well as excellence in doctoral supervision, across the region. YBDTP will fund postgraduate researchers at the Universities of Leeds, 91̽��, York, Bradford, Hull, Leeds Beckett, 91̽�� Hallam and Teesside forming a strong regional training partnership. In YBDTP you'll benefit from a regional doctoral training programme that has interdisciplinary collaboration at its core. The aim is to enable you to develop a range of research skills in biological, biotechnology and biochemical areas as well as equip you with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers.

Funding Notes:

Funding: A tax-free annual stipend at the standard UKRI rate (£19,237 for 2024/25 entry), research costs and tuition fees at UK rate

Academic year: 2025/26

Open to: International (including EU) and UK (home) students

Eligibility:

Open to International (including EU) and UK (home) students.

You can apply if you have, or are expecting to gain, at least an upper second-class honours degree or equivalent. Due to the interdisciplinary nature of this programme, we welcome applications if your background is in any biological, chemical or physical science or mathematics and are interested in using your skills in addressing biological questions. International students should check if they meet the .

We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international (EU and non-EU) applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme. 

The Yorkshire Bioscience DTP is committed to recruiting extraordinary future scientists regardless of age, ethnicity, gender, gender identity, disability, sexual orientation or career pathway to date. We understand that commitment and excellence can be shown in many ways and have built our recruitment process to reflect this. We welcome applicants from all backgrounds, particularly those underrepresented in science, who have curiosity, creativity and a drive to learn new skills.

Not all projects will be funded; a limited number of candidates will be appointed via a competitive process.

English language:

If English isn't your first language, you may need to provide evidence of your English language ability.

How to apply:

Application deadline: Monday 6th January 2025

To submit your application, click on the link to the Expression of Interest form in the Funding Notes section of any of your chosen projects. You can apply for up to 3 YBDTP projects (which can be at different universities).

We would advise you to read the questions in the form before submitting your application. Inside the form there is a link to a document for you to see the questions in advance.

If you have any questions about the application process, please email wrdtp@leeds.ac.uk.

If you have any questions about the project you are interested in, please email the project supervisor (details inside the project description).

How we allocate:

Shortlisting will take place as soon as possible after the closing date and successful applicants will be notified promptly. If you're shortlisted, you'll be invited for an interview on a date to be confirmed in February 2025. You'll be notified as soon as possible after the interview dates whether your application has been successful, placed on a reserve list or unsuccessful. If you are successful, you'll be required to confirm your intention to accept the studentship within 10 days.

Terms and conditions:

The studentships are fully funded for four years, and you must complete your PhD in four years.

You'll receive the UKRI minimum doctoral stipend per year for your living costs, which is paid to you in regular instalments. The  (UKRI) stipend is tax free and does not need to be paid back.

International students will need to have sufficient funds to cover the costs of their , , travel insurance and travel to the UK as these are excluded from UKRI funding.

Funding Notes

To apply for a studentship from the Yorkshire Bioscience Doctoral Training Partnership to undertake this project, please complete an expression of interest form for October 2025 recruitment here: 

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PhD Opportunity: Deciphering the role of biomechanical cues in embryonic cell fate decisions using human pluripotent stem cells

About the Project

The structures that make up the human body trunk, such as the spinal cord and the vertebral column are produced during embryonic development by populations of stem cell-like progenitors (known as neuromesodermal progenitors or NMPs), which are located at the posterior of the embryo. The generation of these progenitors and the cell types they produce is controlled primarily by signalling pathways such as Wnt and Notch. Their disruption leads to impaired NMP production and their derivatives and subsequently disruption of the proper formation of the body trunk, which is often associated with serious birth defects such as spina bifida and vertebral abnormalities. Thus, understanding how signalling pathways direct the induction of NMPs and their distinct derivatives is important for gaining an insight into the causes of such conditions.

This PhD project will examine the role of biomechanical cues, such as differential tissue stiffness, in influencing the response of NMPs to different signalling pathways. We will employ human pluripotent stem cell (hPSC) differentiation as a platform for generating in vitro human NMPs and their derivatives, spinal cord and vertebral column cells together with state-of-the-art mechanobiology approaches to address the following questions:

1) How do Wnt/Notch signalling levels correlate with different NMP cell fate decisions and stiffness levels?

2) How are stiffness levels leading to defined cell fates correlate interpreted at the genomic level?

3) How do Wnt/Notch signalling levels correlate with different NMP cell fate decisions and cell confinement levels?

For informal enquiries about the project, please contact Dr Anestis Tsakiridis a.tsakiridis@sheffield.ac.uk.

About YBDTP:

YBDTP brings together world-class bioscience research and innovation, as well as excellence in doctoral supervision, across the region. YBDTP will fund postgraduate researchers at the Universities of Leeds, 91̽��, York, Bradford, Hull, Leeds Beckett, 91̽�� Hallam and Teesside forming a strong regional training partnership. In YBDTP you'll benefit from a regional doctoral training programme that has interdisciplinary collaboration at its core. The aim is to enable you to develop a range of research skills in biological, biotechnology and biochemical areas as well as equip you with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers.

Funding Notes:

Funding: A tax-free annual stipend at the standard UKRI rate (£19,237 for 2024/25 entry), research costs and tuition fees at UK rate

Academic year: 2025/26

Open to: International (including EU) and UK (home) students

Eligibility:

Open to International (including EU) and UK (home) students.

You can apply if you have, or are expecting to gain, at least an upper second-class honours degree or equivalent. Due to the interdisciplinary nature of this programme, we welcome applications if your background is in any biological, chemical or physical science or mathematics and are interested in using your skills in addressing biological questions. International students should check if they meet the .

We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international (EU and non-EU) applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme. 

The Yorkshire Bioscience DTP is committed to recruiting extraordinary future scientists regardless of age, ethnicity, gender, gender identity, disability, sexual orientation or career pathway to date. We understand that commitment and excellence can be shown in many ways and have built our recruitment process to reflect this. We welcome applicants from all backgrounds, particularly those underrepresented in science, who have curiosity, creativity and a drive to learn new skills.

Not all projects will be funded; a limited number of candidates will be appointed via a competitive process.

English language:

If English isn't your first language, you may need to provide evidence of your English language ability.

How to apply:

Application deadline: Monday 6th January 2025

To submit your application, click on the link to the Expression of Interest form in the Funding Notes section of any of your chosen projects. You can apply for up to 3 YBDTP projects (which can be at different universities).

We would advise you to read the questions in the form before submitting your application. Inside the form there is a link to a document for you to see the questions in advance.

If you have any questions about the application process, please email wrdtp@leeds.ac.uk.

If you have any questions about the project you are interested in, please email the project supervisor (details inside the project description).

How we allocate:

Shortlisting will take place as soon as possible after the closing date and successful applicants will be notified promptly. If you're shortlisted, you'll be invited for an interview on a date to be confirmed in February 2025. You'll be notified as soon as possible after the interview dates whether your application has been successful, placed on a reserve list or unsuccessful. If you are successful, you'll be required to confirm your intention to accept the studentship within 10 days.

Terms and conditions:

The studentships are fully funded for four years, and you must complete your PhD in four years.

You'll receive the UKRI minimum doctoral stipend per year for your living costs, which is paid to you in regular instalments. The  (UKRI) stipend is tax free and does not need to be paid back.

International students will need to have sufficient funds to cover the costs of their , , travel insurance and travel to the UK as these are excluded from UKRI funding.

Funding Notes

To apply for a studentship from the Yorkshire Bioscience Doctoral Training Partnership to undertake this project, please complete an expression of interest form for October 2025 recruitment here: 

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PhD Opportunity: Sh.24.3 PhD in Sound Analysis for Predicting Category 1 Ambulance Calls

About the Project

• Project type: Academic-Led project
• Supervisors: Dr Ning Ma and Professor Jon Barker, University of 91̽��
• Project Partner:  Yorkshire Ambulance Service NHS Trust

Your PhD will focus on the development of voice analysis technologies to enhance the prediction and triaging of Category 1 ambulance calls. 

Ambulance call centres play a critical role in triaging life-threatening medical emergencies. Category 1 calls, indicating life-threatening injuries or illnesses such as cardiac arrest or severe respiratory distress, demand an immediate response to reduce avoidable fatalities. YAS handles over 1.1 million emergency and urgent calls to 999 annually. experienced call handlers often recognise the severity of cases within the initial 15-20 seconds of a call. However, the accuracy of these assessments can be influenced by factors such as the call handler’s expertise, call volumes, and stress levels, potentially delaying life-saving interventions. 

Emerging advancements in artificial intelligence (AI)-driven speech and voice analysis present transformative opportunities to enhance emergency call triaging. For instance, identifying specific audio features, such as laboured breathing or vocal markers of severe distress, could enable earlier and more accurate prediction of Category 1 emergencies. The integration of such tools into call centre workflows promises to improve decision-making speed and accuracy, ultimately saving lives. 

This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies, improving the speed and precision of emergency response systems. The objectives include: 

• Collaborate with YAS to curate a high-quality dataset of emergency call recordings, annotated with corresponding medical outcomes and severity levels. 
• Identify vocal and acoustic biomarkers indicative of life-threatening conditions, including laboured breathing, distressed speech patterns, or cognitive impairment markers. 
• Develop machine learning models capable of predicting Category 1 emergencies based on real-time audio features extracted from calls. 
• Work iteratively with YAS researchers to test and refine the models, ensuring usability, reliability, and integration into operational workflows. 

The successful candidate will benefit from interdisciplinary training in experimental design, advanced speech analysis, and machine learning techniques. Supervision will be provided by experts from the University of 91̽�� and industry professionals at YAS. The candidate will also undertake a placement at YAS to gain hands-on experience in real-world emergency call environments, ensuring the practical relevance and impact of the research. 

This research aligns with the Positive Uses of Sound theme in the Sound Futures CDT and addresses both national and international health priorities in developing fair and inclusive systems for real-world applications.

Please see details on how to apply: 

Funding Notes

• A stipend based on the standard UKRI rate For 24/25 this is £19,237 (25/26 value not yet known)
• A small number of enhanced stipends for widening participation are available. Increased value of £2,000/annum.
• Cost of UK home tuition fees
• A small number of places will fund international fees (the number we can offer is restricted by the funder).
• Research Training Support (RTS), which covers costs of carrying out research such as lab consumables, travel to conferences, access to facilities etc.
• Free access to the CDT’s cohort training such as summer schools We cover travel and subsistence costs.

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PhD Opportunity:  Developing the evidence base for the visual presentation of complex data to clinicians - S3.5-SMP-Alix

About the Project

Innovations using complicated maths such as artificial intelligence promise much in health care, from better diagnosis to better treatment. However, practicing clinicians are rarely experts in these techniques. In fact, little is known on how much practicing clinicians know about areas such as machine learning. In turn, it is not known how information from machine learning should be presented to clinicians. Many of the ways of presenting results from machine learning will look unfamiliar to clinicians. Therefore it is not clear how the complex information these mathematical techniques produce should be presented for it to be interpretable and actionable by clinicians. To address this we will use a medical specialty called clinical neurophysiology as a test case. This specialty focuses on the diagnosis of conditions affecting the brain, nerves and muscles. It produces lots of results, but these are presented in a very traditional fashion such as a table full of values.

Improving outcomes in neurological conditions is impaired by limitations in diagnosis; for example, the NHS Long Term Plan for Neurology begins with a call for “Urgent national action to speed up diagnosis”. Neuromuscular disorders result in muscle weakness, cause significant health problems and can be fatal. They often take a long time to diagnose. Patients have highlighted uncertainty during this period as detrimental to physical and mental wellbeing. To reach a diagnosis, clinicians rely primarily on neurophysiology, where descriptive statistics are used to collect information on nerve and muscle function. Upcoming innovations in clinical neurophysiology, from spectroscopy to multi-electrode recording utilise high-dimensional data creating opportunities for more accurate and timely diagnosis. These data are frequently accompanied by machine learning predictions to enhance diagnostic accuracy. However, practising clinicians are rarely experts in these techniques and may have limited experience reading or understanding these types of data. In fact, although data visualisation is known to be effective in presenting complex data, little is known on practising clinicians’ experience with visualised complex data and machine learning outputs, or how these can support diagnosis effectively and responsibly. Consequently, there is little evidence on effective methodologies to present highly complex information for it to be interpretable and actionable in clinical environments. This proposal aims to address this gap by adapting a user co-created approach. Through interviews and focus groups, we will first understand the ways clinicians in this speciality use data, and their perception and experience with machine learning. This will paint a cohesive picture of clinicians decision-making processes when making a diagnosis and what information they need to support it. We will then develop visualisation prototypes iteratively through design sprints and user experience tests, with ethical considerations such as designs that minimise misinterpretation and induce appropriate amounts of trust in ML results. Large preclinical and clinical datasets are available to build the visualisations. We will first visualise data in current clinical practice, collected from nerve conduction studies and electromyography (EMG).

We will then use data from a novel technique we are developing called ‘optical EMG’. This provides an unprecedented level of information on muscle health, but there are lots of results that are typically presented in formats only familiar to chemists and physicists. Using what we have learnt so far, we will develop visualisations of the results from this new technique. The work will develop new methods and a framework for designing effective and responsible visualisations for complex datasets to support clinical diagnosis, applicable to lots of other medical specialties.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.

Candidates must have a first or upper second class honours degree or significant research experience.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for a period of 3.5 years as well as a research grant of £4,500 to support costs associated with the project.

Please refer to the  for detailed information about the EPSRC DLA and how to apply.

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PhD Opportunity: Accelerating virtual population inference in immuno-oncology through data-efficient multi-fidelity modelling (C3.5-MPS-Xing)

About the Project

Cancer remains one of the most challenging diseases to treat, but recent advances in immuno-oncology (IO) offer new hope. IO therapies aim to use the body's own immune system to fight cancer. However, many IO clinical trials are unsuccessful, due in part to our lack of understanding how an IO treatment interacts with the immune system and the tumour over time. To improve IO treatments, we need better ways to predict how different therapies will work in patients.

This project aims to develop a more accurate and efficient method for doing just that, by combining state-of-the-art techniques in computational modelling and machine learning.

One key problem in cancer treatment is that every tumour is different, not only between patients but also within a single tumour. The tumour microenvironment, which is the area surrounding the tumour, can have a big impact on how well treatments work. Some parts of a tumour may respond to treatment while others resist, making it difficult to predict the overall effectiveness of a therapy.

Existing computational models that simulate tumour growth and response to IO therapies are often too simplistic to capture these complex interactions, or they are so detailed that they take too long to run on a computer.

This project will develop advanced computational methods to simulate how different IO therapies and drug doses affect both the tumour and the patient’s immune system. By combining two powerful approaches - quantitative systems pharmacology (QSP) and agent-based modelling - we can better represent the complexity of tumour-immune interactions and how they change over time.

QSP helps us understand how drugs behave inside the body at the molecular and cellular levels, while agent-based modelling allows us to simulate the behaviour of individual tumour and immune cells and how they interact in different parts of the tumour.

However, these models can be extremely computationally expensive, meaning they take too long to run for practical use in clinical settings. To solve this, we will use machine learning techniques like multi-fidelity fusion and Bayesian optimization. These are advanced methods that allow us to run simulations more efficiently without losing accuracy. In simple terms, multi-fidelity fusion lets us combine results from quick, less detailed simulations with more detailed ones, balancing speed and precision. Bayesian optimization helps us find the best therapy combinations and dosages by making smart choices about which simulations to run next, saving time and computational resources.

This work will create a powerful new tool for predicting how IO therapies will perform in different patients, building on recent work in this area by the supervisory team at the University of 91̽�� and QSP specialists at Certara UK Ltd. It will allow researchers and clinicians to test combinations of therapies and dosages in a virtual setting before moving to actual clinical trials. This could lead to better, more personalized cancer treatments, improving outcomes for patients. The project is suitable for a mathematics, statistics, physics, or computer science student who is keen to learn how mechanistic computational modelling and machine learning can be combined to make progress in biomedicine. The student will be provided with an interdisciplinary training in computational modelling, quantitative analysis and laboratory skills.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application. 

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.

Please refer to the EPSRC DLA webpage [link] for detailed information about the EPSRC DLA and how to apply.

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PhD Opportunity: Developing Digital Biomarkers for Tracking Multiple Sclerosis Progression Using Wearable Devices and AI (S3.5-COM-Sun)

About the Project

Multiple Sclerosis (MS) is a chronic neurological disease that affects over 2.5 million people globally. Mobility impairment, particularly difficulty with walking, is among the most significant challenges for people with MS (pwMS). Early detection of changes in mobility is crucial for adjusting treatment strategies to improve quality of life for pwMS.

Currently, pwMS are assessed during infrequent clinical visits, making it difficult to track subtle changes in their condition. Standard assessments often lack the sensitivity to detect nuanced shifts in mobility or differentiate between varying disease progression types. Consequently, significant changes in a patient’s condition may go unnoticed until the next clinic visit, delaying necessary interventions.

Wearable devices such as smartwatches and fitness trackers offer a promising solution for continuous monitoring. These devices can collect real-time movement data, complementing traditional clinical assessments and giving healthcare providers a more complete view of a patient’s functional status in their everyday life. Continuous monitoring allows for the identification of mobility changes that may not be apparent during brief clinical evaluations. Some studies have investigated the use of wearable data to monitor mobility in pwMS. However, most of this research relies on pre-processed data from device manufacturers, which is often reduced to simplified statistics like minute-level step counts. While useful, these measures do not capture the full complexity of mobility impairments. Other studies focus on walking speed and the number and duration of walking bouts. While these provide insights into walking behaviour, it may overlook nuanced aspects of movement.

This project aims to develop novel digital biomarkers by employing computational techniques such as deep learning and non-linear signal processing to extract detailed mobility information directly from raw sensor data. This data offers high temporal resolution and a comprehensive view of an individual’s movement, capturing the nuances of how a person moves. By analysing this rich dataset, the project seeks to develop novel MS-sensitive digital biomarkers (quantifiable indicators) derived from raw sensor signals that reflect changes in disease progression.

To effectively process high-resolution data from wearable devices, this project will use two advanced signal processing techniques to analyse walking patterns from different individuals. First, the project will align the walking patterns to account for differences in walking speeds. Next, it will break down the complex movement data into simpler components, allowing for the extraction of various specific features of their movement patterns. Finally, this project will develop advanced machine learning models to automatically learn detailed patterns from these components and how these patterns relate to clinical outcomes like The Expanded Disability Status Scale (EDSS), which enables predictions about clinical outcomes.

This project will utilise the Clinical Validation Study (CVS) dataset collected by the Mobilise-D consortium. This large-scale, longitudinal study followed 602 individuals with MS across multiple clinical sites over 24 months. They completed clinical assessments and wore a waist-mounted Inertial Measurement Unit device for seven days to capture real-world walking data, generating the largest longitudinal digital mobility dataset for MS research.

This research will develop objective and continuous body movement measures relating to MS by combining wearable technology with advanced data analytics. This aims to enable earlier detection of important health changes, leading to more tailored treatments and improved quality of life for pwMS, potentially revealing new insights into MS progression and guiding future therapeutic approaches.

This studentship will be based in the Pervasive Computing Research Group, which has strong links with clinicians specialising in multiple sclerosis (MS). The group has collaborated with those responsible for collecting the dataset and conducting patient visits for the Mobilise-D project. This makes it an ideal environment to complement AI and technical analyses with valuable clinical perspectives.

Interested candidates are strongly encouraged to contact the project supervisors (Dr Shaoxiong Sun shaoxiong.sun@sheffield.ac.uk) to discuss your interest in and suitability for the project prior to submitting your application. Please add quote [PHD-AI4MS] in the email subject line.

Candidate Requirements

Candidates must have achieved a minimum 2:1 undergraduate and/or postgraduate masters’ qualification (MSc) in Computer Science, or a relevant subject, by the start of the PhD. The English language requirements must also be met by the start of the PhD.

How to Apply

Please refer to the  for detailed information about the EPSRC DLA and how to apply.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4500 to support costs associated with the project.

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PhD Opportunity: Engineering an in vitro human embryo implantation platform to study pathologies arising in early pregnancy - S3.5-SMP-Lucas

About the Project

Embryo implantation is the step in early pregnancy where the founding tissue of the placenta from the embryo, called the trophoblast, establishes a physical connection with the mother at the inner lining of the uterus, called the endometrium. It is estimated that 75% of pregnancy losses occur during the implantation process. This critical step of early pregnancy, often referred to as a “black box”, has remained poorly understood due to its inaccessibility. However, study of this period is now possible in vitro due to advancements in stem cell-based models of the embryo, blastoids (Yanagida et al. 2021, PMID: 33957081 ), and the endometrium, endometrial assembloids (Rawlings et al. 2021, PMID: 34487490).

The main aim of this PhD project is to establish a human implantation platform that integrates blastoids and endometrial assembloids to study aspects of the embryo and endometrium that impact the implantation process. We have recently generated new pluripotent stem cell lines from single cells of embryos which allow us to study the impact of genetic variation in development(Strawbridge et al. 2022, PMID: 34870826). Concurrently, we have developed a base model of the endometrium that captures features of uterine physiology (Rawlings et al. 2024, PMID: 37402095).

This interdisciplinary project will combine these tools with methods from stem cell biology, embryology, materials science, advanced imaging, and data science to:

1. Investigate the effects of aneuploidies on human embryonic development potential through blastoid generation.

2. Engineer an advanced endometrial assembloid that mimics the natural environment of the uterus.

3. Assess the impact of genetic variation in the embryo on implantation using a blastoid-endometrial assembloid co-culture system.

Outcomes of this work will be benchmarked against the Carnegie Collection of Embryology

() and the Boyd Collection

().

This highly interdisciplinary project will involve collaboration between four complementary research groups, providing the student with a highly desirable skill set spanning health, science, and engineering. Expertise will be drawn from reproductive physiology (Emma Lucas), developmental biology (Stanley Strawbridge), stem cell biology (Ivana Barbaric), and materials science (Frederik Claeyssens). The student will benefit from interactions with the vibrant community of reproductive physiologists, stem cell biologists, and materials scientists. This project represents an exciting opportunity to establish a launching pad to advance our understanding of a critical stage in human reproduction which has the potential to identify pathologies and prescribe interventions, ultimately contributing to better reproductive health outcomes.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.

Candidates must have a first or upper second class honours degree or significant research experience.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for a period of 3.5 years as well as a research grant of £4,500 to support costs associated with the project.

Please refer to the  for detailed information about the EPSRC DLA and how to apply

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PhD Opportunity: Developing innovative, anti-infective peptide-coated calcium phosphate particles for the treatment and prevention of medication-related osteonecrosis of the jaw (MRONJ) - C3.5-DEN-Miller

About the Project

Patients taking bisphosphonates (BPs), a widely used class of drugs for treating osteoporosis and bone cancer, are at an increased risk of developing MRONJ, following oral surgical procedures (i.e., extractions) and spontaneously. In MRONJ, portions of the jawbone die, and the surrounding gum tissue can deteriorate, leaving exposed bone which is prone to infection. Unfortunately, no cure exists for MRONJ, and treatment options remain limited. The condition severely impacts patients, particularly in relation to eating and speaking, complicating the management of their existing illnesses. In severe cases, MRONJ may result in pathological jaw fracture requiring extensive surgical reconstruction. 

This project aims to develop a novel medical device for the preventative treatment of MRONJ. This will be based around materials known as calcium phosphates (CaPs), which are similar to the mineral component of bone. In our laboratory-based studies, these materials have shown success in soaking up bisphosphonates and preventing gum tissue death. 

Currently, MRONJ infections are primarily managed using systemic antibiotics, and research is focused on improving surgical techniques to remove necrotic tissue. Our approach introduces an innovative solution by coating calcium phosphates (mineral component of bone) with small biologically active molecules - peptides. The specific peptide significantly reduces the ability of bacteria to adhere to human cells and has the potential to aid wound closure. Therefore, thecombination of the CaPs ability to bind BPs and the action of the peptide could not only prevent MRONJ occurring but also have the potential to treat MRONJ wounds. 

Infection of the jawbone during MRONJ can be due to various bacterial species. Bacteria will be isolated directly from MRONJ patients and used within our studies. A comprehensive set of laboratory techniques will be employed to evaluate the effectiveness of the peptide coated calcium phosphates, ensuring that they do not negatively affect gum or bone cells and that they are potent in reducing infection of MRONJ-related bacteria. 

During this project the student will learn techniques in material characterisation in in vitro cell culture, microbiology assessment and testing and skills around speaking to patient groups. In addition, they will be exposed to the real whole medical device manufacturing industry, including market analysis and regulation from placements with our industrial partner.

Candidates must have a first or upper second class honours degree or significant research experience. 

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.

Please refer to the  for detailed information about the EPSRC DLA and how to apply.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a £4,500 research grant to support costs associated with the project.

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PhD Opportunity:  Forces in Focus: How do Actin Filament Networks Shape Cellular Mechanics? S3.5-MPS-Peters

About the Project

Living cells maintain and change their shape, adhere to surfaces, spread and divide by harnessing a dynamic filamentous structure termed the cytoskeleton. The primary component of the cytoskeleton is a complex biopolymer network formed of semi-flexible actin filaments, which act in unison with numerous proteins to generate and respond to mechanical forces within cells. What distinguishes the actin cytoskeleton from more traditional polymer networks is that it is both active and polar. The term active refers to the steady consumption of free energy of its components on the nanoscale, and polar to the distinct directionality of each actin filament comprising the network. As a result, actin filaments undergo continuous remodelling, creating complex and diverse architectures within cells. Yet, the relationship between the spatial organisation of such networks and the mechanical properties of cells which they govern remains to be fully understood. Overall, this project aims to explore how both the orientation and connectivity of actin networks on the nanoscale influence the biophysical properties of the cell.  

Key objectives: 

• Develop and implement advanced fluorescence microscopy techniques to visualise the orientational order of actin filaments in live cells. 

• Utilise atomic force microscopy (in collaboration with Prof. J. Hobbs) to map the mechanical properties of cells, in response to differing actin architectures. 

• Develop custom analytical tools to robustly quantify the polarity distribution of actin networks within cells. 

• Integrate findings to give new insights into how actin network orientation underpins cellular mechanics.  

Methodology: 

In this interdisciplinary project, you will receive training in cutting-edge biophysical techniques. You will both use and develop advance fluorescence microscopy methods including orientation resolved single molecule localisation microscopy, to visualise and quantify actin filament orientational order within cells. Simultaneously, you will use atomic force microscopy to directly measure the mechanical properties of cells, such as their stiffness and elasticity, while correlating these measurements with changes in actin network architecture. This integrated approach offers the unique opportunity to explore the fundamental biophysical properties of biopolymer networks and their role in dictating cellular mechanics, with potential implications for cancer research, tissue engineering, and mechanobiology.

Requirements: 

• A strong academic background in Physics, Optics, or Biophysics. 

• Experience in microscopy or a deep interest in microscopy and a strong motivation to master advanced imaging techniques is desirable. 

• Computational skills for image processing and software development (e.g. Python or MATLAB) are highly desirable. 

• Problem solving skills and the ability to work in a highly interdisciplinary team. 

Why us?

91̽�� and the Peters lab prides itself on our diverse, welcoming and supportive research environment. The student will be supported by an excellent supervisory team and will receive mentorship across the School of Mathematical and Physical Sciences and the School of Biosciences. As a Physics of Life Fellow, Ruby has strong links across these two schools and a network of interdisciplinary colleagues, which will enhance and support the PhD student. 91̽�� is a fantastic city, to both study in and live in, with direct links to the Peak District and a vibrant student population.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application. Dr. R. Peters r.peters@sheffield.ac.uk

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.

Please refer to the EPSRC DLA webpage [link] for detailed information about the EPSRC DLA and how to apply.

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PhD Opportunity:  A Tip-everting Endoscope with Adjustable Length, Shape, and Stiffness - C3.5-ELE-Cao

About the Project

Accessing and manipulating objects in confined spaces poses major challenges in robotics, yet these capabilities are crucial across many fields. For example, in medical procedures like endoscopy, tools need to navigate narrow, twisting pathways within the body to reach deep areas for diagnosis or surgery. Similarly, search-and-rescue robots must maneuver through debris in collapsed buildings, and industrial inspection robots need to navigate tight pipelines or machinery.

A promising solution to these challenges is the soft-growing robot, which can lengthen its body by everting pre-inverted thin-film tubing material from its tip. This unique mechanism generates frictionless movement, as there is no relative sliding between the robot and its environment. Thus, soft-growing robots can advance through confined spaces without pushing against surfaces, reducing the risk of damaging delicate tissues or fragile structures. This type of frictionless navigation makes soft-growing robots particularly promising for tasks requiring high maneuverability and safety, such as medical navigation.

However, while soft-growing robots show potential, they also face several engineering challenges: (1) Retraction: Retracting or “shrinking back” the robot after it has extended is essential for applications requiring backtracking or repositioning. Achieving retraction without creating friction or unwanted movement is challenging due to the unique inverted structure. (2) Shape Morphing: Confined spaces often have complex, winding pathways or branches. Shape morphing is essential to allow the robot to follow these curves closely without getting stuck or causing harm. Yet, controlling the shape of a growing tube with precision is technically challenging. (3) Variable Stiffness: While soft robots need flexibility to navigate complex pathways, they also need rigidity for certain tasks, like manipulation or providing stability during procedures. Existing soft-growing robots are often too soft to handle external loads effectively.

This project aims to develop the next-generation endoscope that is capable of adjusting its length, shape, and stiffness. This new endoscope will enable safe, dexterous navigation through complex anatomical pathways and offer sufficient rigidity for delicate surgical manipulations, expanding the potential for minimally invasive procedures.

This project includes the following specific objectives: 

Obj1: Development of a Shape-Morphing, Variable-Stiffness Backbone

Obj2: Integration of Backbone with Tip-Everting Skin

Obj3: Development of a Robotic Endoscope

The ultimate goal is to develop a fully functioning endoscope prototype and conduct rigorous bench-top/ex-vivo testing, supported by clinical and industrial partners at 91̽�� Children’s Hospital and our industry partner.

The novelty of this project lies in creating a single endoscopic device with rapid fricitonless locomotion, adaptive shape morphing, and variable stiffness—capabilities not yet achieved in medical robotics. This technology is potential to transform the ways to reaching and manipulating targets in confined spaces such as human body or pipelines, promoting safer and more effective operations in these environments.

The student will have the opportunity to work closely with medical doctors and industrial partners for this project.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application. 

We require applicants to have either an undergraduate honours degree (1st/2:1) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution.

Please refer to the  for detailed information about the EPSRC DLA and how to apply. On the DLA application, make sure that you enter the code from the project title. Do not apply for the standard School PhD.

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant to support costs associated with the project. The amount available from the EPSRC grant for research costs is £4,500 total across the lifetime of the award.

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PhD Opportunity:  Beyond the helix: new insights into the structure, function and design of RNA therapeutics using biophysics technologies and deep learning (C4-CMB-Pyne)

About the Project

Therapeutic RNAs and vaccines are being developed for a broad range of human diseases. However, their optimization is hindered by mRNA instability and inefficient protein expression. mRNA secondary structure plays an integral role in the biosynthesis of proteins [1-2] as well as molecule stability [3]. Therefore, the higher order structure of RNA has an important role on the biological activity. There is currently a significant lack of robust analytical methods or biophysical approaches that provide significant insight into mRNA structures. 

We have developed high-resolution atomic force microscopy (AFM) methods that are unique in their ability to provide quantitative information on nucleic acid structure, function, and interactions in liquid with sub-molecular resolution without the need for labelling or averaging [4-5]. Our preliminary data shows that we can achieve equivalent resolution on RNA molecules, which forms the basis of this ambitious project in collaboration with AstraZeneca. 

We have developed a single-molecule analytical pipeline which combines atomic force microscopy (AFM) and deep-learning image analysis methodologies to quantify the formation of complex nucleic structures with nanometre resolution [5-6]. Our pipeline can discriminate between molecular topologies (e.g. linear vs circular), and determine molecular shape, conformation, and aggregation for individual DNA molecules within a larger population. 

This proposal will be a collaboration with AstraZeneca to develop a novel pipeline for the structural and functional characterisation of novel mRNA therapeutics using high-resolution, single particle, atomic force microscopy (AFM) imaging in combination with optical tweezers and HPLC [7]. Our pipeline has the potential to accelerate research and development into RNA therapeutics, providing currently inaccessible structural data showing changes in the secondary and tertiary structure of RNAs. We can carry out these measurements at distinct time points after assembly, providing insight into the stability of RNA structures. We will obtain automated quantitative measurements of structure, aggregation and morphology, and link these back to changes in manufacturing of formulation design, allowing AZ to link quantitative structural parameters to their manufacturing processes in a new, faster feedback loop.

This project will aim to answer the fundamental question, how does mRNA structure impact protein production? 

The objectives are to: 

• Use biophysical methodologies to determine the structure and stability of a test set of mRNA molecules, which have different predicted structures but encode for the same protein
• Develop a pipeline to build up our understanding of structure function relationships in mRNA by bridging biophysical structural data to functional data from other methodologies
• Test this pipeline on new emerging mRNA candidates to determine whether we can improve the mRNA manufacturing feedback loop 

Candidate Requirements

Candidates must have achieved a minimum 2:1 undergraduate and/or postgraduate masters’ qualification (MSc) in Chemical Engineering, or a relevant subject, by the start of the PhD. The English language requirements (overall IELTS grade of 6.5 with a minimum of 6.0 in each component, or equivalent) must also be met by the start of the PhD.

How to Apply

Please refer to the  for detailed information about the EPSRC DLA and how to apply.

Funding Notes

The award will fund the full (UK) tuition fee and UKRI stipend (currently £19,237 per annum) for 4 years, as well as a research grant of £4500 to support costs associated with the project.

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PhD Opportunity:  S3.5-MAC-Bonci - Tremor in Parkinson’s disease subtypes: objective measures and classification

About the Project

Following Alzheimer, Parkinson’s disease (PD) is the most common neurodegenerative condition, with the fastest-growing incidence worldwide [1]. Parkinson is a clinically highly heterogeneous condition, although some common features are observed in people with PD: rigidity, bradykinesia (i.e., slowness of movement), and tremor [2]. The latter symptom, although it is not life-threatening, often represents the earliest occurrence of this condition and can affect patients’ ability to perform daily-life activities such as self-care, eating, writing and getting dressed.

Subtle changes and daily fluctuations of tremor cannot be captured during one-off assessments using clinical scales and, therefore, wearable devices have been adopted in the attempt to obtain objective quantifications of tremor [3].

For a better understanding of both this condition and evaluating new treatment efficacy, identifying PD subtypes is paramount. Based on clinical observations, two main subtypes of PD have been identified: non-tremor and tremor-dominant PD, which usually presents only subtle balance instability/gait impairments [2]. Independently of disease duration or age, a recent study has identified that brain tissue information (i.e., mitochondrial complex deficiency) allows for a clear identification of these subtypes [4].

Therefore, for widespread adoption and use of this PD stratification, the PhD student will undertake a programme of study to combine tremor characterization using cutting-edge techniques and wrist wearable devices, with existing clinical scores and novel biomarkers based on brain functional imaging spectroscopy and blood samples. To this aim, they will use data collected using wearable devices both in laboratory and real-world acquisitions.

This is the first study attempting PD stratifications based on tremor and functional imaging. Due to the worldwide incidence of people with PD and the requirement to identify a cure, innovative engineering approaches and healthcare technologies are essential to stratify and classify PD patients before their inclusion in clinical trials.

The student will obtain extensive experience in state-of-art algorithms to quantify tremor and other mobility outcomes using wearable devices, as well as in other technologies adopted in human movement biomechanics, and, generally, in neurodegenerative conditions. Additionally, they will build a strong foundation of transferable skills essential for a wide variety of future careers. The successful candidate will work in a supportive and collaborative environment under the supervision of Dr Tecla Bonci, Dr Jennifer Rowson, and Prof Oliver Bandmann from the 91̽�� Institute for Translational Neuroscience and Neuroscience, School of Medicine and Population Health. While aiming at a holistic characterization of PD patients, the work carried out within this project will be complemented by other projects focusing on mobility outcomes quantified during real-world walking using the recent work tackled by the  consortium. Moreover, the candidate will be part of the vibrant multi-disciplinary Integrative Musculoskeletal Biomechanics () team, which includes RAs and PhD students, and the  Institute. This project will be conducted in collaboration with the 91̽�� Teaching Hospital and the 91̽�� Biomedical Research Centre (BRC), providing access to clinical datasets and expertise in neurodegenerative conditions.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application. 

Please refer to the EPSRC DLA webpage  for detailed information about the EPSRC DLA and how to apply.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4,500 to support costs associated with the project.

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PhD Opportunity:  S3.5-MAC-Cheong - Multiscale modelling of the murine growth plate

About the Project

The growth plate (epiphyseal disc) is a thin but complex structure found at the ends of long bones of growing children. Trauma to the growth plate has been known to interfere with growth. This can result in premature fusion of the growth plate, causing discrepancy in limb length and angular deformity. Yet, there is little information on how load transfers across the whole of the growth plate, which is important for the prevention of injuries to the growth plate. For example, shear fracture of the proximal femoral growth plate results in slipped capital femoral epiphysis, which affects 1 in 1,300 adolescents in the UK, putting them at higher risk of developing osteoarthritis later in life.

In this project, computational modelling will be used to understand the mechanical stress/strain transfer across the bone and growth plate in a mouse model. The aim of this PhD project is to develop finite element (FE) models of the growth plate to map the stress/strain distribution across microstructures in the growth plate. Digital volume correlation (DVC) - in situ mechanical loading test results have already been acquired, and will enable us to use this data to validate FE models of the mouse growth plate for the first time. The computational models will then be applied to identify regions at highest risk of fracture and develop strategies for injury prevention.

The project will involve image processing to develop models of the growth plate, by improving an algorithm for automatic segmentation that was recently been developed by the team. FE models will be developed from previously acquired images of the mouse tibiae at different level of details. Hence, the student will gain experience working with large files of computed tomography (CT) images (desktop and high resolution synchrotron micro-CT), while performing research in this interdisciplinary project to answer important preclinical and clinical questions.

Requirements

We are looking for applicants who are eager to develop new skills and apply their existing knowledge. You should be excited to learn and develop new technology, be passionate about the subject and look to be creative in your work. In addition, you should:

- Hold (or will receive this year) a master's degree (or equivalent, e.g. MEng) at merit/distinction level, or a first class/ upper second (2:1) class honours degree in biomedical engineering, mechanical engineering, applied maths or a related field.

- Proficiency in image processing techniques, finite element analysis software (e.g., Abaqus, Ansys etc), and programming languages such as Python, MATLAB etc.

- Strong analytical and problem-solving skills.

- Good interpersonal and organisational skills, and the ability to work as a team player.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application. 

Please refer to the EPSRC DLA webpage  for detailed information about the EPSRC DLA and how to apply.

Supervisors: , 

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4,500 to support costs associated with the project.

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PhD Opportunity: S3.5-MAC-Dardeno - Enhancing personalised medicine via ecosystems of digital mirrors

About the Project

Digital mirrors – mathematical refinements of digital twins – are predictive models that simulate real-time states of physical systems and are recognised as crucial for personalised healthcare in the UK. Although their adoption in healthcare lags behind other sectors, on an individual basis, these models have advanced to the point of implementation for performance optimisation of medical devices; likewise, progress has been made for using these models to enhance orthopaedic surgical planning and postoperative care. As mentioned, advancements have largely been at the individual-scale. However, creating customised models is expensive and has significant environmental impacts caused by the heavy demands of data collection and model training/development [1]. A more efficient method involves sharing information between similar systems to build a network (or ecosystem [2]) of models, which also supports the UK’s efforts to reduce carbon emissions [3].

This project aims to adapt digital-mirror technologies to ecosystems (for example, from a single hip implant to a group of hip implants). Data and expert knowledge will be used to build detailed statistical models that reflect the diverse characteristics and relationships within groups. For similar systems, a multilevel approach will be implemented, pooling parameters across models to facilitate knowledge transfer. Emphasis will be placed on understanding the unique physical phenomena specific to each system. Machine-learned mappings/functions that leverage the abstraction of these physical and digital systems to geometric spaces [4] will then be learnt to connect to systems that are more diverse, utilising intermediate structures/models (e.g., physics-based models) as needed, to allow for transfer across the entire ecosystem. This functionality will allow informed predictions and decisions for structures/groups across ecosystems for which there are no, or limited, data available. For instance, it may be beneficial to transfer information from a demographic of elderly hip replacement patients, for whom there are extensive data, to a younger cohort, for whom data are limited. These concepts have shown promise for monitoring the health of infrastructure but remain completely unexploited for the healthcare sector.

While this concept of scaling from individuals to ecosystems is pertinent for all digital-mirror applications, this project will focus on developing technologies to address critical problems in orthopaedics. Orthopaedic conditions differ significantly from one individual to another, and are influenced by factors such as age, bone density, muscle strength, and lifestyle. Personalised healthcare can allow for treatment plans to be tailored to the unique characteristics of each patient, thus improving patient outcomes. In procedures such as hip replacements, digital mirrors could be utilised preoperatively to determine the most suitable implant size, reducing both the duration of surgery and material waste. Post-surgery, these models can also be used to project the course of recovery and to guide management strategies, facilitating the monitoring of the healing process and the long-term health of the bone-implant interface and adjacent tissues and structures. By developing a digital-mirror framework that enhances prediction accuracy while reducing costs and environmental impact, this project seeks to enable wider adoption and accessibility of personalised healthcare in orthopaedics. The candidate will liaise with healthcare professionals to identify and acquire appropriate data from multiple sources such as wearable devices, bone scans, etc. Considering these multiple data sources, they will develop new digital-mirror technologies for orthopaedic concerns, using physics-based, statistical, and mathematical modelling approaches. The ideal candidate will have a background in engineering, computer science, mathematics, and/or physics. While specific expertise in all aspects of the project is not required, they should have a strong desire to expand their knowledge in unfamiliar areas (with support from supervisors and in-house collaborators).

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application. 

Please refer to the EPSRC DLA webpage  for detailed information about the EPSRC DLA and how to apply.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4,500 to support costs associated with the project.

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PhD Opportunity: Advancing ECG Interpretation with Human-Centred Multi-modal AI (S3.5-COM-CChen)

About the Project

Cardiovascular diseases (CVDs) are the leading global cause of mortality, accounting for a staggering 32% of all deaths. Despite the immense potential of electrocardiograms (ECGs) for early and equitable diagnosis, physicians demonstrate an alarming average interpretation accuracy of only 54%[1]. Moreover, medical errors have been increasingly recognized as a critical public health issue, currently reported as the third leading cause of death in the United States [2].

This PhD project seeks to tackle these pressing healthcare challenges through pioneering Human-Centred Artificial Intelligence (HCAI) approaches. While there have been preliminary AI solutions for ECG digitisation, diagnosis, and prognosis, a recent AI-ECG survey published in 2024 [3] has highlighted significant shortcomings in existing tools, including poor image quality, bias, and inadequate validation.

Understanding that both human practitioners and AI systems are susceptible to errors, this project will innovate adaptive and robust HCAI applications, which aims to develop adaptive applications that combine human and machine intelligence to overcome limitations.

Key Objectives:

1-Innovative Use of Diverse Types and Sources of ECG Data for Improved Robustness:

This project will extend beyond existing AI-enabled ECG systems, which primarily rely on clean, digital ECG signals. Our aim is to expand AI capabilities to include traditional paper

ECGs—whether in the form of scanned documents, photos, or screenshots—formats that are far more prevalent in clinical practice and educational resources than digital waveforms. By enabling AI to process and interpret these diverse data types from multiple sources, we will ensure that ECG-HCAI applications are equipped with robustness to adapt effectively to the varied sources encountered in real-world clinical settings, while also enriching AI capabilities with a broader range of contextual information.

2-Multimodal Learning Integration for Enhanced Decision-Making:

In addition to ECG data, this project will investigate the advantages of multimodal learning, including large language models and adaptive learning techniques. By incorporating clinical knowledge, clinician inputs (such as reports), and patient histories, we aim to develop precise ECG interpretation personalised to individuals, leading to a more comprehensive and personalised analysis of cardiac health.

3-Development of an Explainable HCAI Tool:

A key focus of this PhD project is to leverage large language models to develop an explainable HCAI tool for ECG diagnosis and prognosis. This tool will provide clinicians with clear, interpretable insights, thereby fostering collaborative decision-making between clinicians and AI systems. By prioritising transparency and usability, we aim to empower healthcare professionals while promoting the equitable expansion of reliable cardiac ECG diagnostics globally.

About the main supervisor, Dr. Chen: 

Candidate Requirements

Candidates must hold/achieve a minimum of a merit at master’s degree level (or international equivalent) in a Computer Science or a relevant subject, by the start of the PhD. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. The English language requirements must also be met by the start of the PhD.

How to Apply

Please refer to the  for detailed information about the EPSRC DLA and how to apply.

Interested candidates are strongly encouraged to contact the project supervisor, Dr. Chen Chen, at [enquiry.ai4health@gmail.com], to discuss their interest in and suitability for the project prior to submitting their application. Please include the quote '[PHD_EPSRC_AI4ECG]-Your Current Programme-Name' in your email. Please send me your CV (grade, ranking, research experience included), transcripts, first-authored papers/thesis (please merge all into one single file) and other supporting materials (such as slides to show your research experience, or github website to show your past projects).

Emails or inquiries that do not adhere to this guideline may not receive a response. Due to the high volume of emails I receive, emails without strictly having this quote in the subject title may be automatically classified as spam and ignored. So please help me to catch up your email. I am sorry that it is difficult to provide feedback to every applicant. Only shortlisted candidates will be noticed. Please understand that failing to receive my response does not reflect your qualifications or potential for other PhD programs.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4500 to support costs associated with the project.

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PhD Opportunity: GlucoVox: Voice Analysis for Non-Invasive Diabetes Glucose Monitoring Using Machine Learning (C3.5-COM-Ma)

About the Project

Background

Diabetes is a leading cause of preventable death and affects over 500 million people worldwide, with nearly half unaware of their condition. Effective management requires regular blood glucose monitoring. While finger-prick testing is common, it is painful, inconvenient, and requires frequent testing. Continuous glucose monitoring (CGM) provides real-time tracking but has limitations, including cost, skin irritation, and accessibility, especially for older adults or in low-resource settings.

Diabetes also increases the risk of cognitive decline, particularly in those with long-term diabetes or frequent low blood sugar (hypoglycaemia). Symptoms of hypoglycaemia, such as confusion, slower reaction times, and speech changes, are often first noticed by others. Fluctuating glucose levels can further impair memory and cognitive function, complicating traditional monitoring methods for some patients.

Advances in speech technology offer new opportunities for health monitoring. Acoustic analysis, a technique that examines speech characteristics, has been successfully used to monitor conditions like Parkinson’s disease, depression, and cognitive decline. Recent studies suggest that diabetes may also impact vocal characteristics (, ) and low blood sugar may result in slower or slurred speech (, ). These findings suggest that voice analysis could be a non-invasive, low-cost alternative for monitoring glucose levels.

This project aims to develop advanced voice technology to identify vocal markers and temporal speech patterns associated with blood sugar levels, with a particular focus on hypoglycemic ranges and cognitive changes in elderly individuals with diabetes. You will employ advanced speech technology to analyse voice data. Voice samples, including spontaneous and planned speech, will be collected from people with diabetes at different glucose levels, from an ongoing project, and evaluated through a neuropsychological battery. You will explore state-of-the-art deep learning models to detect pauses and speech patterns in the voice that correspond to glucose changes and cognitive deficits. By personalising these models, the system will learn to predict glucose levels based on unique voice characteristics of each individual.

This research is novel in its exploration of voice as a biomarker for blood glucose levels, especially considering the cognitive dimensions linked to hypoglycaemia in elderly patients. As the number of people with diabetes continues to rise, there is a pressing need for non-invasive, easy-to-use, and affordable monitoring tools (). The project offers potential for an accessible tool for accurate diabetes management and cognitive screening, aligning well with the increasing demand for innovative health technologies.

Cross-disciplinary Collaboration

You will be supported by a cross-disciplinary supervisory team:  from the School of Computer Science (machine learning and speech technology),  from the School of Medicine and Population Health (clinical diabetes), and  from Imperial College (consultant in diabetes and technology). The project also intersects key research themes within several research centres such as the  and is supported by , where diabetes is a key theme. This partnership will ensure the project’s relevance to real-world diabetes care and drive the project’s translational impact.

Interested candidates are strongly encouraged to contact the project supervisors (Dr Ning Ma n.ma@sheffield.ac.uk) to discuss your interest in and suitability for the project prior to submitting your application. 

Candidate Requirements

Candidates must have achieved a minimum 2:1 undergraduate and/or postgraduate masters’ qualification (MSc) in Computer Science, or a relevant subject, by the start of the PhD. The English language requirements must also be met by the start of the PhD.

How to Apply

Please refer to the  for detailed information about the EPSRC DLA and how to apply.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4500 to support costs associated with the project. 

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PhD Opportunity: User Simulation-Grounded Automatic Conversational Depression Diagnosis Support (C3.5-COM-Wang)

About the Project

Depression, a form of depressive disorder, is increasingly common among the population, representing a significant mental health concern. However, appropriate and timely treatment for depression can often be unavailable due to limited access to screening services. Without timely treatment, depression can worsen and have a longer impact on individuals. Recently, along with the development of artificial intelligence (AI) and machine learning (ML) techniques, especially the recent emerging large language models – large neural network models that are pre-trained on a vast amount of text data to learn the patterns, relationships, and structures of language -- we have observed the growth of automatic depression screening and diagnosis solutions due to their advantages in timely treatment, remote access and symptom tracking availability. To leverage the recent advancements, this study targets the investigation of natural language modelling for effective automatic depression screening.

The main challenges while addressing automatic conversational depression screening services include (1) the limited support of depression screening services, (2) the reproducible, publicly available creation, use and sharing of data, and (3) the development of effective alternatives to the clinical interview for depression screening. The data collected for depression screening and diagnosis is sensitive, and often not publicly available, resulting in a reproducibility concern of many studies. 

In this research project, we aim to address the above challenges by proposing a research pipeline to develop automatic conversational depression screening services and relying on user simulation and synthetic data generation, which can be publicly available with minimised ethical concerns. The introduction of conversational depression screening agents can effectively replace the common self-report questionnaires or screening tools by enabling multi-turn interactions for comprehensive symptom descriptions. In addition, the developed conversational systems can mitigate the shortage of clinical interviews, especially in the context of low- and middle-income countries, when joined with timely follow-up treatment.

Supervision Team

This project will be co-supervised by Dr Xi Wang (primary supervisor) and Dr Ning Ma (Secondary Supervisor) and supported by Rotherham Doncaster and South Humber NHS Foundation Trust (RDaSH). 

Dr Xi Wang is a Lecturer in Natural Language Processing in the Department of Computer Science at the University of 91̽��. His research interests include Conversational AI, Retrieval-Augmented Generation, and the intersection of NLP and IR. He is a member of the 91̽�� NLP group, one of the largest and leading language processing groups in the UK (ranked 3rd according to csranking.org).

Dr Ning Ma is a Joint Lecturer in Medical Computing at the School of Computer Science, University of 91̽�� and the Academic Directorate of Medical Imaging and Medical Physics at the 91̽�� Teaching Hospitals NHS Foundation Trust. His research interest lies in the application of multi-modal AI in healthcare. He serves as the Insigneo Institute theme co-director for Healthcare Data/AI and the N8 Centre of Excellence in Computationally Intensive Research theme lead for Machine Learning.

Candidate Requirements

We are seeking self-driven candidates with a strong interest in conducting research in conversational AI for healthcare, encapsulated with strong programming, communication and presentation skills. Candidates with relevant research project experience are preferred.

Applicants should hold a first-class undergraduate honours degree, an MSc with Merit or Distinction, or an equivalent qualification in computer science or related field from a recognised institution. 

Interested candidates are strongly encouraged to contact the project supervisors (Dr Xi Wang xi.wang@sheffield.ac.uk), including [PhD-EPSRC-DLA] in the email subject line, to discuss your interest in and suitability for the project prior to submitting your application.

How to Apply

Please refer to the  for detailed information about the EPSRC DLA and how to apply.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4500 to support costs associated with the project.

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AI-Enhanced Healthcare - Leveraging Multimodal Data Integration for Impactful Innovation (S3.5-ELE-Eissa)

About the Project

In recent years, the integration of Artificial Intelligence (AI) has emerged as a transformative force within this discipline, bringing with it a promise of revolutionising healthcare by enabling data-driven decision-making and predictive modelling. This AI-driven revolution is set to impact every facet of healthcare, from diagnostics and treatment to patient monitoring and personalised care.

One of the most striking applications of AI in healthcare is in diagnostics. AI-driven image analysis systems have shown exceptional accuracy in detecting diseases like cancer. These systems can process and interpret medical images with incredible speed and precision, often surpassing human capabilities. Radiologists are now being assisted by AI algorithms that can highlight potential abnormalities, ultimately leading to more accurate and early diagnoses. This not only reduces the chances of misdiagnosis but also helps in the timely initiation of appropriate treatment.

Moreover, Natural Language Processing (NLP) algorithms are being deployed to analyse vast volumes of medical records and clinical notes. This textual data holds a wealth of information about patient histories, treatment outcomes, and disease trends. AI-powered NLP helps identify patterns and correlations that might have otherwise gone unnoticed. Physicians and healthcare providers can thus gain valuable insights into patient care, improving decision-making and treatment planning.

Beyond the clinical setting, engineers are leveraging AI to personalise treatment plans. By analysing a patient's genetic makeup, medical history, and lifestyle factors, AI can help tailor therapies to individual profiles. This precision medicine approach not only enhances treatment effectiveness but also reduces the risk of adverse reactions to medications, making healthcare safer and more efficient.

AI's role in engineering also extends to wearable health devices. These devices, equipped with sensors and AI algorithms, can monitor patients in real-time. They track vital signs, activity levels, and other health parameters, providing early warnings of potential health issues. This proactive approach to healthcare allows individuals to take control of their well-being and seek timely medical attention when needed. Engineers are continually refining these wearable devices to make them more accurate, user-friendly, and accessible.

In summary, the fusion of AI and biomedical engineering is paving the way for a future where healthcare is not only more effective but also more accessible and patient-centred. It's a future where diseases can be detected earlier, treatments are personalised, surgeries are safer, and patients are empowered to take charge of their health. The potential of AI in healthcare is boundless, and engineers play a pivotal role in realising this potential.

The selected candidate for the research team's innovative project will embark on a journey to advance data engineering solutions tailored specifically for healthcare applications. In an era characterised by the proliferation of health-related data and the advent of transformative technologies, there is an urgent need for novel data engineering methodologies. These methodologies must efficiently process, integrate, and extract insights from diverse data sources, ranging from medical images and electronic health records to real-time sensor data.

The PhD project will be a challenging yet rewarding endeavour, involving the design and implementation of cutting-edge data engineering frameworks. These frameworks will facilitate seamless data integration, real-time processing, and informed decision-making in the dynamic and complex landscape of healthcare contexts. By contributing to this field, the candidate will be at the forefront of advancing healthcare, making it not only more effective but also more efficient and patient-centric. Ultimately, the intersection of engineering, AI, and healthcare promises a brighter and healthier future for us all.

We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) or equivalent in a relevant science or engineering subject from a recognised institution. The University’s standard  requirements must also be met.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.

Please refer to the  for detailed information about the EPSRC DLA and how to apply. On the DLA application, make sure that you enter the code from the project title. Do not apply for the standard School PhD.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4,500 across the lifetime of the award to support costs associated with the project.

 

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Energy Efficient Secure Hardware Acceleration of AI Models for Edge Devices in Healthcare Applications (S3.5-ELE-Benaissa)

About the Project

The healthcare domain is inherently characterised by its sensitivity and the critical importance of data privacy and security. The need to ensure the confidentiality, integrity, and availability of healthcare data cannot be overstated. As AI models are increasingly applied to this data-rich environment, the vulnerability to data breaches and unauthorised access becomes a significant concern.

The successful candidate will delve into the intricate realm of healthcare data security, striving to design and fortify data pipelines against potential threats.

This endeavour goes beyond standard security practices. It involves not only the development of robust encryption and access control mechanisms but also an exploration of countermeasure techniques to safeguard information.

While the potential of AI in healthcare is profound, its practical implementation often demands high computational and energy power especially for real-time processing capabilities. This is particularly critical for decision support systems, where timely insights can make a crucial difference in patient outcomes.

Hardware acceleration offers an avenue to meet the computational demands, however to meet the energy demands dedicated optimisations in hardware are necessary which in turn requires careful investigation of the algorithmic and architectural design space.

The successful candidate will explore the efficient deployment of AI models onto dedicated hardware platforms, optimising their performance, security and energy efficiency for real-time applications. This entails the development of hardware-software co-design strategies, ensuring that AI algorithms operate seamlessly within the constraints of the particular healthcare application. This will not only impact the academic and healthcare communities but also extend to benefit society at large by promoting the responsible and secure integration of AI in healthcare, aligning with the ethical imperatives of our modern era.

We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) or equivalent in a relevant science or engineering subject from a recognised institution. The University’s standard  requirements must also be met.

Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.

Please refer to the  for detailed information about the EPSRC DLA and how to apply. On the DLA application, make sure that you enter the code from the project title. Do not apply for the standard School PhD.

Funding Notes

The award will fund the full (UK or Overseas) tuition fee and UKRI stipend (currently £19,237 per annum) for 3.5 years, as well as a research grant of £4,500 across the lifetime of the award to support costs associated with the project.

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PhD Opportunity: Longitudinal effects of exercise or pharmacological treatments on the mechanical properties of the human femur

Supervisors: 

Prof E Dall’Ara, Dr VS Cheong, Prof R Eastell

Short description: 

Osteoporosis is a skeletal disease that decreases bone strength and increases the risk of fracture. Current diagnostic tools are not accurate enough in stratifying patients at high risk of fracture who need treatment.  In this project we will develop an image-processing and computational modelling pipeline to assess the risk of fracture in patients and evaluate the effect of anti-Osteoporosis treatments on the bone strength and risk of fracture. 

Start and Duration: 

October 2025; 3.5 years

Funding: 

Home students (co-funding needed for International students) 

Application Process: 

Email Prof Enrico Dall’Ara (e.dallara@sheffield.ac.uk)

Application deadline: February 2025 (date To be decided) 

Link to application (if available): not available yet, will be published in January 2025 but feel free to contact Prof Dall'Ara for any questions.