Vacancies

Bone cancer primarily occurs in children, adolescents and adults over the age of 65 years. Current treatment of choice involves chemotherapy, tumour removal and replacement with an implant (endoprosthesis). With advancement in cancer treatment, patients are living longer and there is a need for implants with improved durability and longevity.

Porous implants fabricated by additive manufactured technology (3D printing) offer a promising avenue for enhancing bone ingrowth and improving clinical outcomes. However, clinical data regarding the optimal implant geometry is currently inconclusive. Because implant integration is intrinsically linked to the quality of the surrounding tissue, understanding time-dependent changes in stress distribution within the bone is vital to inform implant design.

The purpose of this PhD is to investigate key parameters of porous implants to improve bone ingrowth. The student will develop musculoskeletal models and finite element analysis (FEA) models from clinical images and gait analysis data. The student will perform mechanical testing of the implants to validate the FEA models.

The successful candidate will work in a supportive and collaborative environment, with clinicians and strong industrial support, including a placement at an orthopaedic implant manufacturer. The student will join the vibrant multi-disciplinary Integrative Musculoskeletal Biomechanics (IMSB) team, which includes academics, postdoctoral researchers, and PhD students from the Insigneo Institute for Healthcare Technology.

Requirements

We are looking for a self-motivated student who wants to develop new skills and apply their existing knowledge. You should be independent, excited to learn, solve problems, passionate about the subject and creative. In addition, you should:

• Hold a Master's or MEng degree at merit/distinction level, or a first class/upper second (2:1) class honours degree in mechanical engineering or bioengineering.
• Good interpersonal and organisational skills, and the ability to work as a team player.
• Proficiency in Finite Element Analysis software (e.g., Abaqus, Ansys), image processing techniques, and programming languages such as Python and Matlab.

Interested candidates are strongly encouraged to contact Dr Vee San Cheong, to discuss your interest in and suitability for the project prior to submitting your application.

Supervisory team:

Dr Vee San Cheong (primary supervisor) is a UKRI Future Research Leaders Fellow with expertise in developing novel methods to study bone biomechanics. In collaboration with researchers and surgeons based at the Royal National Orthopaedic Hospital (RNOH) and Insigneo Institute, she has been studying bone mechanics using innovative X-ray imaging techniques and FEA in the context of boneremodelling to predict the effect of implant design, drugs and exercise on bone adaptation.

Dr

Vee San

Cheong

Lecturer in Biomechanics
School of Mechanical, Aerospace and Civil Engineering

Research interests: multiscale modelling of musculoskeletal tissues in response to novel biomaterials | optimisation of implant designs & treatment interventions for orthopaedic diseases

Email

v.cheong@sheffield.ac.uk

Telephone

+44 114 2220159

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Prof Paul Fromme (UCL) has extensive experience and publication track record in applied mechanics and ultrasound. Building on ongoing clinical trials at the RNOH, he has been using innovative ultrasound techniques and FEA to evaluate bone growth around bone- anchored limb prostheses, which offer superior gait and joint function compared with prosthesis.

Dr David Simpson (industrial supervisor) leads the limb salvage service for Adler Ortho UK Ltd and works with orthopaedic surgeons across a range of UK hospitals.

PhD start Date:

1 February 2027

This studentship will cover the full UK (home) rate and fund an annual tax-free maintenance stipend at the standard UKRI rate (£21,805 in 2026-27) for up to 4 years, as well as a research grant to cover additional costs of training, courses, project costs, conferences and travel.

PDRA in MRI for Diabetes assessment

Funding Notes

The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.

Funding Notes

The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) plus an industry-funded stipend enhancement for 4 years, as well as a research grant to support costs associated with the project.

Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Prof Nick Turner and Prof Nicholas Warren.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Biological recognition elements like antibodies and enzymes power everything from diagnostics to drug delivery. Yet, their real-world impact remains constrained by environmental instability and immunogenicity.

Molecularly Imprinted Polymers (MIPs) are changing that. As robust, highly adaptable synthetic alternatives, MIPs selectively bind proteins, drugs, and toxins across applications in biosensing, targeted therapies, and purification. Conventional development of MIPs relies on computationally expensive simulations followed by slow, manual experimentation that limits effective optimization.

This fully funded 3.5-year PhD project breaks new ground by bringing data-driven machine learning, molecular modelling, and high-throughput automation into synthetic polymer design. You won’t just make materials; you will redefine how they are discovered.

The Project Scope

Working at the cutting edge of polymer chemistry, your research will focus on developing custom MIP hydrogels engineered for two core medical challenges:

• Signal Absorption: Designing smart materials capable of absorbing specific bacterial biochemical signal molecules.
• Controlled Release: Engineering hydrogel frameworks for precise, targeted drug delivery.

Rather than relying on traditional synthetic workflows, you will hybridise computational modelling, data-driven experimental design, with robotic automation and batch analytics to rapidly explore polymer space, dramatically accelerating material discovery.

World-Class Infrastructure & Collaboration

You will join a vibrant interdisciplinary environment hosted jointly across the Discipline of Chemistry (School of Mathematical and Physical Sciences) and the Discipline of Materials (School of Chemical, Materials and Biological Engineering). You will engage with:

• Machine Intelligence Expertise: Collaborate closely with the Sheffield Centre for Machine Intelligence to build and refine predictive ML algorithms.
• Healthcare Impact: Conduct research within the renowned Insigneo Institute, driving innovation at the intersection of medicine, engineering, and science.
• Automated Labs: Gain hands-on experience with state-of-the-art high-throughput synthesis platforms and advanced analytical instrumentation.

These are supported by exceptional polymer synthetic and characterisation facilities.

Comprehensive Skills & Career Training

This studentship offers rigorous, multi-faceted training designed to build an exceptional career profile in modern chemistry and materials science. You will gain direct expertise in:

• Advanced polymer synthesis and material characterisation
• Bioassay design and high-throughput experimentation
• Robotic lab automation, data science, and programming
• Academic dissemination, patenting, and commercialisation strategies

To prepare you for leadership roles in industry or academia, you will receive tailored career development, options for teaching and mentoring, and full financial support to present your research at national and international conferences. The supervisory team maintains a track record of high-impact publishing and active integration into global AI-chemistry, biological and polymer networks.

Candidate Profile

We are seeking a highly motivated candidate with a strong background in Chemistry, Materials Science, Chemical Engineering, Bioengineering, or a related discipline.

• Required: A passion for interdisciplinary research, strong foundational chemistry knowledge, and enthusiasm for computational methods and laboratory automation.
• Desirable: Prior experience in polymer science, data analysis, or computer programming is advantageous, though comprehensive training in these tools will be provided.

Ready to pioneer the next generation of smart materials?

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

Funding Notes

The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.

Funding Notes

The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.

Funding Notes

The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.

PhD Opportunity: Multiscale mechanics of cytoskeletal networks and genome integrity during cell division (S3.5-MPS-Peters)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Ruby Peters and Prof Alice Pyne.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Background:

Cell division requires cells to generate and precisely coordinate mechanical forces to separate their chromosomes between two daughter cells. The cytoskeleton is a dynamic network of protein filaments that generates and transmits these forces, allowing cells to change shape and physically move chromosomes during division.

We know a great deal about the individual building blocks of the cytoskeleton, including their structural and mechanical properties. However, a major challenge is understanding how these properties are transformed as they become filaments and are organised into dynamic networks, and how this ultimately drives biological function. Recent work has highlighted an important role for cytoskeletal filament networks in protecting genome integrity during cell division. We hypothesise that the nanoscale organisation of these networks controls how mechanical forces are transmitted to chromosomes and that this contributes to faithful chromosome segregation. This project will determine how cytoskeletal architecture influences DNA organisation during cell division, combining advanced fluorescence microscopy and automated image analysis approaches.

Aims:

• Map how cytoskeletal networks are organised around the genome during cell division.
• Determine how network architecture manipulates the genome through mechanical interactions.
• Establish how cytoskeletal organisation protects genome integrity during chromosome segregation.

Experimental approach:

Cytoskeletal network organisation and chromosome behaviour will initially be investigated using advanced fluorescence microscopy in mammalian cells undergoing cell division. Super-resolution microscopy will be used to visualise cytoskeletal filaments and chromosomes at the nanoscale, while live-cell imaging will follow their organisation and dynamics through mitosis and anaphase. Custom image-analysis tools will be developed to quantify filament density, orientation, connectivity and spatial relationships with chromosomes. These spatial maps will then be correlated with measurements of cellular stiffness via Atomic Force Microscopy. Data from these experimental approaches will be used to inform network-based and coarse-grained computational approaches to decipher how changes in cytoskeletal architecture influence the distribution of mechanical forces across the dividing cell. Model predictions will be tested through targeted perturbations of cytoskeletal organisation in dividing cells, linking network architecture to chromosome behaviour and genome integrity.

Outcomes:

This project will establish a multiscale framework linking the properties of individual cytoskeletal components to filament architecture, molecular structure, mechanics and genome integrity during cell division. Through this framework the project will provide new insight into how complex biological systems transform the properties of their molecular building blocks to achieve targeted cellular function.

The project will also generate open computational methods for quantitative analysis of biological filament networks and nanoscale microscopy data, enabling structural and mechanical measurements to be integrated across length scales. These approaches will provide transferable tools for understanding how the organisation of dynamic biopolymer networks gives rise to mechanical function in living systems.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

Funding Notes

The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.

PhD Opportunity: Teaching AI How Proteins Move: Experiment-Guided AI for Predictive Molecular Science (in collaboration with Exciting Instruments Ltd.) (C3.5-MPS-Craggs)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Timothy Craggs and Prof Haiping Lu.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Proteins are often depicted as fixed three-dimensional structures, but they constantly move between different shapes. These movements determine how proteins function, interact with other molecules and bind drugs. Some important shapes exist only briefly and cannot be seen in a single static structure.

Artificial intelligence has transformed protein structure prediction. Although newer models can generate multiple conformations, they learn mainly from databases of static structures and computer simulations, with little direct experimental information about how real proteins move in solution.

This PhD will develop AI methods that learn from single-molecule fluorescence experiments. Single-molecule Förster resonance energy transfer (smFRET) uses two fluorescent labels attached to selected positions on a protein. Energy transfer between the labels acts as a molecular ruler. Measuring individual proteins reveals distinct conformations that would be hidden by averaged measurements, along with their relative populations and, where possible, how rapidly they interconvert.

The student will assemble a benchmark panel of soluble proteins with previously measured conformational changes. Published data for maltose-binding protein and heat-shock protein 90 will provide independent tests. Rep helicase, which switches between four experimentally resolved conformations, will be the main prospective system, building on the team’s existing expertise and data. Klenow fragment, a well-studied DNA polymerase domain, provides an alternative if required.

The student will develop a computational link between a predicted three-dimensional ensemble and the fluorescence distributions expected experimentally. This will allow measurements to constrain and improve a selected AI model, rather than serving only as a pass-or-fail test after prediction.

Because several ensembles can explain the same fluorescence measurement, atomistic molecular dynamics (MD) simulations will provide an independent check that experimentally compatible predictions also represent realistic molecular motion. Where necessary, enhanced sampling methods such as replica-exchange MD or umbrella sampling will help the simulations explore conformations indicated by the experiments.

The project will culminate in prospective tests. The model will predict equilibrium conformations and populations for held-out labelling pairs and solution conditions. Selected fluorescence measurements will then test and refine these predictions. Rates of motion will be inferred only where supported by the data. As an optional final stage, the student may investigate whether the model can identify the labelling positions that would provide the most useful new information.

Success will not depend on training a very large model from scratch. An existing protein model, published measurements and the team’s Rep helicase expertise provide a strong foundation, while a small number of new experiments will offer controlled tests. The project aims to deliver an experimentally benchmarked method for predicting conformational ensembles across the protein panel. Findings will be published and research outputs shared where appropriate, following consideration of intellectual property, confidentiality and commercialisation. More broadly, the work could advance understanding of protein function and support future biotechnology and drug-discovery applications.

Applicants may come from chemistry, physics, biochemistry, computer science, mathematics, engineering or related disciplines. Expertise in machine learning, physical modelling and single-molecule experiments is not expected at entry. Tailored training will cover AI, molecular simulation, molecular biophysics, quantitative analysis and experimental design.

The student will work across the Schools of Mathematical and Physical Sciences and Computer Science, with access to the University’s AI Research Engineering support and specialist single-molecule facilities. They will also undertake an industry placement with Exciting Instruments, gaining experience in automated scientific instrumentation, reproducible software and translating research into practice.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: From Data to Decisions: Graph World Models for Reinforcement Learning (S3.5-ELE-Darvariu)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Victor-Alexandru Darvariu and Prof George Panoutsos.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Artificial intelligence (AI) is increasingly being used to solve complex decision-making problems. One of its most powerful approaches is reinforcement learning, where a computer learns what actions to take by interacting with a system and receiving feedback (called rewards) based on how well it performs. Reinforcement learning has been used in areas ranging from robotics and games to the tuning of large language models. It also has growing potential in engineering and science since, under the hood, many problems involve making a sequence of decisions under uncertainty.

This project focuses on problems that have a network (“graph”) structure. A graph represents a system as a collection of objects and the relationships between them: for example, machines connected through a manufacturing process, stations connected through links in a transportation network, or components interacting in a scientific system. Many real-world engineering problems naturally take this form.

At present, applying reinforcement learning to such problems usually requires a human expert to translate the real-world system into a precise mathematical description. This creates a major barrier. Domain experts may have large amounts of data about a system but may not have the specialist expertise in AI needed to formulate the system in the way reinforcement learning algorithms require. Furthermore, hand-designed mathematical descriptions may fail to capture complex system dynamics and reflect important patterns that are present in the data.

A promising alternative is to build a “world model”: an AI model that learns the behaviour of a system directly from observations of it. Rather than relying entirely on human-designed formulations, the world model learns an internal representation of the system and how it changes over time. World models are attracting considerable attention in recent AI research, particularly for visual environments such as robotics and game-playing. However, existing approaches are largely designed for image inputs and do not naturally capture the complex relationships found in graph-structured systems.

The first aim of this PhD will therefore be to develop new world model architectures specifically for graph-structured data. The central challenge is to learn representations that simultaneously preserve relational structure, model how graph structure and features evolve, and retain the relevant information required for downstream decision-making. The second will be to investigate how these learned representations can best be used for decision-making. For example, should the learned model translate observations into a conventional mathematical description, or should reinforcement learning take place directly inside the learned representation? The project will develop and systematically compare these different approaches in terms of performance, data efficiency, generalisation, and computational costs.

The final stage of the project will demonstrate the methods on a real engineering challenge. The initial application will be the scheduling of batch manufacturing processes, where decisions must be made about which tasks should run on which pieces of equipment and when. Better decision-making in such systems can improve the use of equipment, energy, time and other resources.

The project sits at the intersection of AI, machine learning, and engineering. It would suit a student interested in developing fundamental AI methods while also testing them on challenging real-world problems.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Trustworthy AI: understanding predictive performance via sampling approximation (in collaboration with Wayland Additive) (C3.5-ELE-Panoutsos)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Prof George Panoutsos and Dr Inaki Esnaola.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

In recent years we have observed tremendous growth in the engineering applications of machine learning to accelerate and assist tasks in a range of industry sectors. This growth has also revealed a fundamental challenge and a barrier in the use of such machine learning tools in critical sectors. The challenge of 'trustworthiness'. The narrative in machine learning is gradually shifting from 'striving to be faster/more accurate/more sophisticated' to 'do we understand sufficiently, hence trust, decisions and outcomes derived from machine learning?'. A central challenge is therefore to move beyond empirical validation and provide quantitative measures of predictive reliability. In particular, theoretical generalisation bounds can describe how well a learned model is expected to perform on unseen data, but their practical value depends on whether the associated quantities can be estimated accurately and efficiently. In the context of engineering applications, this could relate to, for example, simple predictive tasks, such as regression and classification. For critical applications (e.g. related to human safety, defence and security), not being able to guarantee outcomes is a barrier to adoption. Aerospace parts manufactured with the assistance of machine learning cannot be certified for flight, nor can healthcare decision-making algorithms be qualified for use in clinical practice, unless full accountability and robustness of outcomes and machine learning performance can be guaranteed.

Statistical learning theory, leveraging information-theoretic tools, has provided, to an extent, robust theoretical bounds and generalisation guarantees for such machine learning models. The computational evaluation of such bounds, as well as sampling from the associated complex distributions, remains a severe bottleneck (as these deem the associated methods as impractical to use). It is timely to conduct research on these topics as success would unlock the use of a class of machine learning tools in critical applications, yielding both significance and impact.

The aim of this project, in collaboration with Wayland Additive, is to develop efficient Langevin-dynamics-based sampling methods for estimating the quantities required by information-theoretic generalisation bounds (Years 1-2). The successful development of this framework will be subsequently applied to empirically validate these theoretical bounds in real-world, industry-relevant case studies (Years 2-3) in advanced manufacturing. We expect the research to yield world-leading outputs, contributing to machine learning and its use in engineering applications more broadly, while paving the way for impact in collaboration with our industrial partners.

Candidate profile

We welcome applicants with a strong background in mathematics, statistics, computer science, electrical and electronic engineering, or a related discipline. The project will particularly suit a candidate with interests in probability, optimisation, machine learning theory, Bayesian inference, or uncertainty quantification. Strong programming skills, ideally in Python, are expected; prior experience with stochastic sampling, information theory, or machine learning research is desirable but not essential

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Agentic tools for digital twin simulations, discovery and analysis (S3.5-COM-Richmond)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Prof Paul Richmond and Edwin Brown.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Digital twins are computer-based representations of real-world systems. They can be used to explore how complex systems behave, test ideas, and understand what might happen under different conditions. However, building and using these simulations can require specialist programming and significant expertise. This project will investigate how recent advances in artificial intelligence can make digital twins easier to create, explore and improve, while also enabling new approaches to scientific discovery.

The project will use FLAME GPU, a high-performance simulation framework designed to model large numbers of interacting entities, such as people, animals, vehicles or other components of complex systems. The research will extend this framework with artificial intelligence systems that can understand and respond to natural language. This could allow a researcher to describe a system or experiment in ordinary language and have an intelligent system create an initial simulation, run it, examine the results and suggest improvements.

The research will investigate intelligent software agents that can work with simulations as active research tools rather than simply answering questions about them. For example, an agent could be asked to model the spread of a behaviour through a population. It could construct a model, run experiments, compare the results with observations from the real world, identify where the model does not agree with those observations, and then propose and test changes to improve it. This creates the possibility of a more iterative and interactive approach to developing computer models.

A key part of the research will be understanding how artificial intelligence systems can be reliably connected to simulations and scientific knowledge. Potential research areas include methods for allowing agents to retrieve and use relevant information from existing models, documentation and experimental data; natural-language methods for controlling simulation parameters and experiments; automated approaches for designing experiments and exploring possible model configurations; and techniques for validating and calibrating simulations against real-world observations. The project will also investigate how simulation results can be represented so that researchers can ask meaningful questions about them using natural language.

More broadly, the project will explore how intelligent agents can reason about models, use computational tools, assess uncertainty, challenge their own assumptions and work collaboratively to investigate complex systems. This could lead to new frameworks for interactive and agent-driven digital twins, where researchers can move naturally between describing a problem, creating a model, running experiments, examining evidence and refining their understanding. It offers opportunities to develop both fundamental new methods and practical software that could change how complex computational models are created and used across areas such as engineering, environmental science, transport, biology and social systems.

The project will work closely with the Research Software Engineering Team at the University of Sheffield, providing opportunities to explore both the research and industrial applications of agent-driven digital twins. The student may have opportunities to undertake secondments with the RSE team, contribute to industrially funded research projects, and gain experience in developing robust, research-grade software for real-world applications. This combination of doctoral research, professional software engineering practice and industrial engagement will provide strong preparation for careers in research software engineering, scientific computing and industrial research and development, with potential pathways into RSE roles within academia, industry or research organisations.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Reducing medical testing burden with AI (S3.5-COM-Newman-Griffis)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Denis Newman-Griffis and Dr Midhun Parakkal Unni.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Do you want to explore the potential of artificial intelligence (AI) in medicine? This funded studentship offers an exciting opportunity to shape how AI technologies can reduce a significant cost to the NHS, and source of burden for patients: unnecessary laboratory testing.

The NHS conducts more than 300,000 lab tests every day, and nearly 40% of these are estimated to be unnecessary. Studies around the world have shown that millions of unnecessary lab tests are costing vast quantities of time, money, and carbon every year. Estimates of patient impact show that 1 in 4 patients receiving testing are subject to unnecessary tests, rising to 3 in 4 for patients awaiting surgery.

Unnecessary testing is a solvable problem, and the NHS has highlighted the transformative power of data and AI as a key way to help tackle the issue. Early studies have shown high potential value for AI-enabled identification of unnecessary tests, but these methods lack the explainability and sensitivity to the variability in individual patients needed to be put into practice.

This project builds on pilot work conducted by the supervisory team which investigates a key dimension left out of most AI research on unnecessary testing: time. The complex interactions between a patient’s health data over time are essential for understanding which tests need to be conducted when, and for explaining why a decision to test or not is recommended.

Key objectives for the project include:

- Develop new, explainable AI methods for identifying potentially unnecessary lab tests, using time series modelling and the latest AI developments.

• Explore the exciting combination of multimodal health data for complex pathology modelling, including medical histories, free-text clinical notes, and large-scale tabular medical data.
• Characterise how predictive models perform for different types of lab testing and in different patient populations, to map the evolving frontier of AI research for clinical pathology.
• Engage with clinical partners to explore translating your research findings into applicable AI tools, with the potential for affecting real-world pathology decisions in the NHS.

We aim to use the findings of this project to inform collaborations with the NHS on reducing costs and improving patient experience. The project will enhance your skills in both AI and clinical understanding, deepening your professional insight and opening new doors for enhancing your career. You will have the opportunity to publish and present your research at leading international conferences for AI, machine learning, and health informatics, as well as in the University of Sheffield’s thriving AI for Health community.

You will join the University of Sheffield’s Centre for Machine Intelligence (CMI), a leading interdisciplinary research centre at the cutting edge of responsible AI in practice. You will be supervised by Dr Denis Newman-Griffis (AI for Health Lead) and Dr Midhun Parakkal Unni (AI for Health Fellow).

Students in the CMI are valued and active members of the Centre and vital in contributing to our aims and helping to effect real change around AI in practice. You will be part of a wider multidisciplinary network of PhD students spanning the CMI’s four themes: Human-Centric AI, AI-Enabled Research, Autonomous Systems and Robotics, and AI for Engineering and Manufacturing. You will have the chance to influence and lead CMI activity, seminars and events, and meet leaders in the field.

You will also be part of the wider School of Computer Science, where you will engage with world-leading academics in machine learning, multimodal AI, natural language processing, and complex systems. You will also have the opportunity to participate in the world-leading UK Open Multimodal AI Network (UKOMAIN), led by the University of Sheffield.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Engineering Targeted Lipid Nanoparticles for RNA Delivery to Treat Rare Life-Threatening Vascular Disease (S3.5-HES-Al-Ahmady)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Zahraa Al-Ahmady and Prof Arshad Majid.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Some rare inherited vascular diseases make arteries and other tissues dangerously fragile. People affected by these conditions can experience sudden arterial tears or ruptures, sometimes at a young age, and treatment is often limited to monitoring, risk reduction, and emergency management. New genetic medicines could potentially address the underlying biological causes of these diseases, but they face a major practical challenge to enable safe delivery of RNA-based medicine efficiently to the appropriate cells within blood vessels.

This PhD project aims to tackle this delivery challenge. The student will develop tiny, lipid-based carriers known as lipid nanoparticles (LNPs). These particles can protect RNA therapeutics in the bloodstream and facilitate their delivery into cells. LNPs have transformed the delivery of some RNA medicines, but most current formulations preferentially accumulate in organs such as the liver. Rare vascular diseases therefore require a different solution: nanoparticles designed specifically to recognise and enter cells within the vessel wall.

The project will use vascular COL4A1/COL4A2- and COL3A1-related disorders as disease models. These are severe, potentially life-threatening inherited conditions in which abnormalities in collagen can result in marked tissue and vascular fragility. The associated research programme proposes RNA-based approaches to silence harmful gene transcripts and promote the production of functional collagen. This PhD will focus on the engineering challenge that is essential for such treatments to succeed: refining the nanoparticle delivery system so that therapeutic RNA reaches vascular and collagen-producing cells efficiently and safely.

The student will refine LNP formulations using microfluidic methods and investigate how particle composition, size, surface properties, and targeting molecules influence biodistribution and cellular uptake. Targeting strategies will include molecules that recognise markers expressed by vascular endothelial cells and receptors associated with smooth muscle, stromal, or extracellular-matrix-producing cells. More advanced approaches may incorporate two targeting functions within the same delivery system to improve localisation and retention in diseased vascular tissue.

Promising nanoparticles will first be evaluated in relevant human cell models, including vascular cells and, where available, patient-derived cells. The student will assess cellular uptake, RNA activity, cell safety, and the duration of therapeutic effects. The most promising formulations will then be evaluated for circulation, biodistribution, and vascular targeting in appropriate preclinical models. Advanced imaging and tissue analyses will be used to determine whether successful delivery results in meaningful molecular and structural improvements.

The project brings together nanomedicine, biomaterials engineering, RNA therapeutics, and vascular biology. Its wider goal is to establish design principles for delivering RNA medicines beyond the liver and into the vascular system. Although inherited collagen disorders provide a compelling initial application, the resulting technology could support the development of future treatments for other rare and severe vascular conditions for which there remains a substantial unmet clinical need.

The student will join an interdisciplinary research environment with established expertise in LNP–RNA formulation, gene therapy, vascular and extracellular-matrix biology, advanced imaging, and translational evaluation. They will gain a distinctive combination of skills spanning nanoparticle engineering, molecular biology, quantitative characterisation, and preclinical therapeutic development.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Surgical Capsule-size Devices for Non-invasive Gastrointestinal Therapies (in collaboration with IntroMedic and Sheffield Teaching Hospital) (C3.5-ELE-Damian)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Dana Damian and Dr Chen Chen.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Today, patients can swallow a small capsule camera (like PillCam) that takes pictures of the digestive system. These are primarily diagnostic tools that enable clinicians to observe the gastrointestinal tract., but these devices are passive: they cannot move with precision, take tissue samples, or perform surgical interventions. Our goal is to move beyond traditional "see-only" functionality by combining diagnostic imaging with active robotic intervention capabilities. Such “see+do”capsules can transform them into miniature robotic surgeons that can treat diseases inside the gastrointestinal (GI) tract non-invasively.

This project, in collaboration with IntroMedic and Sheffield Teaching Hospital, will develop tiny, capsule-sized robots that are swallowed, wirelessly controlled from outside the body, and coordinated to perform surgical tasks. These capsules would navigate through the GI tract, perform procedures such as tissue cutting or sampling, and then safely degrade so that they leave the body naturally.

Why this matters

Healthcare systems worldwide face growing pressure due to an ageing population and a shortage of trained medical staff. Endoscopic and colonoscopic procedures are widely used but they can be invasive, cause trauma, and often cannot reach deep or folded regions of the intestine. Current capsule endoscopes can record images but cannot interact with tissues.

By creating swallowable robotic systems with true dexterity and intelligence, we could:

• Perform surgeries or biopsies without incisions or hospital stays.
• Reach areas currently inaccessible to conventional tools.
• Reduce the burden on medical professionals by partially automating procedures.
• Provide safer, more efficient, and less traumatic treatments for patients.

This vision is bold, and there are several scientific, technical and ethical challenges:

Miniaturisation with dexterity: Each robot must be small enough to swallow, yet still carry mechanisms to cut, sample, or anchor.

Power supply: Conventional batteries are too big and unsafe for long operations inside the body. We must use wireless energy transmission instead.

Safety and sustainability: The robots should have a level of biodegradability and leave the body without risk.

Coordinated intelligence: Multiple DOFs and capsules must be controlled and coordinated in complex biological environments using imaging or biological sensing.

What makes this project possible now

Until recently, it was impossible to power and coordinate multiple miniature devices inside the body. Our group has a strong track record and recent breakthroughs in surgical device design, capsule robotics and miniaturisation, and wireless power transfer. We also have strong collaborations for biomaterial synthesis, wireless magnetic control and powering technology, AI, and solid clinical and industrial partnerships in capsule endoscopy.

What the project will do

As a PhD researcher, you will work on three main areas:

1. Wireless power and control (Year 1): Advancing our technology to allow capsules to be powered and controlled inside the body using safe magnetic fields.
2. Capsules design (Year 2): Designing and fabricating the capsules themselves, including camera modules, cutting tools, and anchoring systems, using biodegradable materials.
3. Coordinated and collective intelligence (Year 3): Creating algorithms and control methods so that multiple capsules or capsules with multiple-DOFs can perform surgical tasks semi-autonomously using imaging/biosensing.

The PhD student will receive extensive training in fabrication techniques, wireless magnetic control, embodied intelligence-based design, AI and clinical translation to generate biological evidence which is of interest to scientists and the clinical community alike. They will work alongside engineers and clinicians in a diverse, inter-disciplinary team to benefit from a range of expertise to deliver this project.

Broader impact

This project aims to redefine what surgery means: moving from large instruments and invasive operations to miniature, intelligent robots working inside the body, making healthcare safer, faster, and more widely accessible. Nonetheless, the technology could extend far beyond, e.g., repair aircraft, pipelines, or other hard-to-reach environments.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Developing an in-vitro 3D musculoskeletal model for the drug development pipeline (S3.5-CMB-Green)

Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Nicola Green and Prof Fred Claeyssens.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Musculoskeletal (MSK) conditions are a primary cause of global disability, affecting approximately 22% of the world's population. As global populations age, the societal and healthcare burden of these conditions is escalating significantly. Unfortunately, current clinical treatments offer limited long-term relief, while the development of new therapeutics is severely limited by a lack of predictive, human-relevant testing platforms. Traditional 2D cell cultures fail to reproduce the complex 3D architecture, biochemical signals, and mechanical forces experienced by cells in living tissue, while animal models suffer from poor physiological translation to human disease, high costs, and major ethical concerns. To accelerate drug discovery there is an urgent unmet need for physiologically relevant 3D human tissue models.

This project also directly supports UK strategic goals to reduce animal testing, and you will be equipped with high-demand practical expertise across 3D bio-printing, advanced tissue culture, material testing, and lab-based drug screening. Mastering these cutting-edge techniques will make you exceptionally competitive for high-value careers in the pharmaceutical, biotechnology, and medical technology sectors.

Project Aims and Methodology

In this project, you will refine existing prototype scaffolds into an integrated biphasic model matching the porosity and stiffness of native bone and muscle. You will seed human cell lines across the interface, allowing realistic tissue maturation. After applying dynamic mechanical loading using custom bioreactors, you will use this platform to study tissue crosstalk and validate your model using current MSK therapeutics.

Training and Skill Development

You will gain extensive multidisciplinary skills bridging engineering and life sciences. Specialist technical training will encompass biomaterials manufacturing (3D printing, emulsion templating), advanced material characterisation (scanning electron microscopy, FTIR, mechanical testing), 3D cell culture, histology, immunohistochemistry, qPCR, and bioreactor control.

In addition to project supervision, we offer rigorous research and professional development skills training - including experimental design, data and statistical analysis, time management, and oral and written communication - by the supervisory team and through the Doctoral Development Programme (DDP). There are also opportunities for you to obtain valuable training and experience in areas such as commercial awareness and technology translation or public engagement, science communication and media awareness.

You will become a valued an active member of the wider multidisciplinary biomaterials research network, with the opportunity to influence and lead activities in this network. You will present your work at regular departmental seminars in preparation for taking your work to national and international conferences. We also have a strong community of PhD researchers here to help you to adjust to life as a PhD student.

Supervisory Team and Candidate Profile

You will be co-supervised by Dr. Nicola Green and Prof. Fred Claeyssens within the School of Chemical, Materials and Biological Engineering (SCMBE), joining a supportive research environment with links to the INSIGNEO Institute for Healthcare Technology and the Healthy Lifespan Institute (HELSI).

We welcome applicants from diverse backgrounds across the physical sciences, biological sciences, or biomedical engineering.

How to 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.

Please refer to the EPSRC DLA webpage for detailed information about the EPSRC DLA and how to apply. Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Generating a population based paediatric upper body skeletal atlas for personalised bone strength prediction using principal component and finite element analysis (S3.5-MAC-Li)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Xinshan Li and Dr Pinaki Bhattacharya.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

We are excited to announce a new PhD opportunity in the areas of computational modelling, principal component analysis, finite element analysis, with a chance to also learn about experimental biomechanics. This project is in collaboration with the Sheffield Children’s NHS Foundation Trust. The successful candidate will join a collaborative and world-leading team of researchers within the Integrated Musculoskeletal Biomechanics (IMSB) group and the Insigneo Institute for Healthcare Technology in Sheffield.

Digital twins (DTs) are digital replica of an object that in healthcare can be used to assist clinical decision making and risk stratification. They have been successfully applied in areas such as the more accurate prediction of osteoporotic fracture in elderly, but there are very few DTs for children due to scarcity of child-specific imaging and clinical data. On the other hand, recent UK Government policies have highlighted children and young people’s (CYPs) health and wellbeing as key priority areas for improvement, featured in the Future 10 Year Health Plan.

The aim of this project is to use the unique post-mortem (PM) CT imaging dataset from the ongoing ChildBone project to create a population based paediatric upper body skeletal atlas for personalised computer modelling and bone strength analysis. The specific objectives are:

• To generate age-based mean shape and density models for ribs and arm bones (ulna, radius, humerus) for ~150 children aged 0-16 years old. Smaller age range (every 3 months) will be used for early childhood whereas larger range (every 1 year) will be used for older children.
• To conduct principal component analysis and determine the main modes of variations in terms of shape and density for each age range and investigate how they change with growth and development.
• To carry out finite element analysis on personalised arm and rib bones to determine bone strength (failure load) under a variety of loading conditions.
• To perform statistical analysis on the biomechanical and shape/density biomarkers to determine any correlations with respect to demographic and clinical information (height, weight, gender).
• To make the upper body skeleton atlas open-access through scientific publications and report your findings at national/international conferences such as the European Society of Biomechanics Conference and BioMedEng Conference.

The main outcome is to achieve an enhanced and quantitative understanding of the morphology and biomechanics of paediatric upper body skeleton. In the long term, these population-based models will be used for rapid creation of personalised DTs using 2D X-Ray measurements (length and width) that are abundant in the clinic. These personalised DTs can be used to investigate undetermined fracture scenarios, improve design of car seat and surgical planning for complex radius fractures.

The team led by the main supervisor has developed pioneering engineering approach to predict paediatric bone strength based on CT scans to further our understanding of bone mechanics in children (PMID: 42461900). Through the on-going ChildBone project (EPSRC EP/Z536118/1, 2025-28), a unique digital atlas of paediatric bone models (>100 cases) and a dataset of CT scans have been created. Up to 43 bones have been expertly segmented for each child. On-going prospective recruitment in the next two years will see a further 50 PM children being recruited to expand this dataset. Rib samples will also be collected to undergo mechanical testing (micro-indentation and micro-CT imaging) for model validation. An automatic segmentation algorithm has also been trained to segment paediatric arm bones (radius and ulna) from CT scans with high accuracy.

How to apply

Interested candidates are strongly encouraged to contact Dr Xinshan (Shannon) Li 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. Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Quantum sensors for host–pathogen interaction: measuring phagosomal pH during immune evasion by intracellular bacteria (S3.5-MPS-Cadby)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Prof Ashley Cadby and Dr Joe Tyler.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

When immune cells called macrophages swallow invading bacteria, they trap them inside a small membrane-bound compartment and then rapidly acidify it, turning it into a hostile, acidic environment designed to kill the intruder. This drop in acidity is one of the immune system's fastest and most effective weapons. But some pathogens fight back by disrupting or slowing that process, so understanding exactly how they do it depends on being able to measure acidity accurately, inside a single compartment, in a living cell, in real time.

That is harder than it sounds. The usual tools are fluorescent probes: dyes or proteins that change how brightly they glow, or how long they glow for, as acidity shifts. The trouble is that brightness is an unreliable messenger. It fades as the probe bleaches under the microscope, changes with how much probe has built up, and is distorted by the crowded, variable environment inside a cell. So, although these probes are widely used, they cannot easily deliver a true, reliably calibrated reading from a single compartment.

This project takes a different approach, borrowed from quantum sensing. Rather than reading out acidity as a brightness, we will read it out as a frequency, a property that does not care how bright your probe is, how much of it there is, or how long you have been imaging. We will use a sensor protein whose internal electronic structure absorbs microwaves at a frequency that shifts predictably as its immediate chemical surroundings become more acidic. Because that shift is measured as a frequency rather than an intensity, the technique is naturally immune to the artefacts that limit conventional fluorescent probes.

You will help build a custom microscope from the ground up, combining precision light detection, sensitive single-photon counting and microwave delivery into one working instrument: a serious piece of quantum instrumentation and an excellent grounding in optical physics and electronics. Once built you will then use it to calibrate the sensor protein's response across the full range of acidity found in cells, and check that the readout is not thrown off by temperature or salt concentration, the kind of everyday variation any real biological measurement must survive. Finally, they will take the sensor into living macrophages, attach it to the compartment where bacteria are digested, and watch, in real time and in a single organelle, exactly how acidic that environment becomes, benchmarking the new method directly against the dyes currently in use.

This is a rare opportunity to work right across the boundary between physics and biology. Techniques like this have, until recently, mostly needed specialised low-temperature equipment; it is only in the last few years that sensor proteins working at room temperature have made this kind of quantitative, single-compartment chemical imaging genuinely achievable. The student will graduate with hands-on experience of building optical and microwave instrumentation, of quantitative spectroscopy, and of cell biology and immunology, a combination that is genuinely difficult to get from a single PhD, and one that opens doors into both fundamental quantum sensing research and applied biomedical imaging.

No prior experience of quantum physics or cell biology is required, just curiosity about both, and a willingness to get equally comfortable in the optics lab and at the cell culture bench.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Untangling the Role of RNA Packing in the Cell (S3.5-MPS-Harris)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Prof Sarah Harris and Prof Stuart Wilson.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Reading the genetic information encoded within DNA produces very long messenger RNA (mRNA) molecules, which can create significant topological problems for the cell. Long biopolymers are at risk from shear forces and from degradation by RNA cutting enzymes. Moreover, these mRNAs often need to be transported to other regions of the cell for processing, and it is important that these molecules do not get so entangled in other cellular machinery as they are being transported.

To solve this problem, the cell uses sticky RNA binding proteins to neatly fold the RNA into a compact shape. In Sheffield we have recently developed a biochemical toolkit to put chemical cross-links into these compacted RNA particles, which enables us to study how the RNA is folded up inside the cell. We can now also use genetic engineering to remove key RNA binding proteins known to mediate the RNA folding process and observe how this influences the formation of the compacted RNA particles. However, these experiments do not allow us to visualise the structures of the folded protein-RNA complexes within the compacted particles.

We will develop computer simulations that use polymer physics to simulate the protein-guided folding of the RNAs. We will then compare the results of the simulations with the experimental data from chemical cross-linking to test the validity of the models, enabling us to iteratively improve our understanding of the folding process. This will provide us with molecular-level insight into the physical interactions that enable the compaction of mRNAs, and enable us to explain the changes observed when specific RNA binding proteins are absent.

This project is suitable for students from a physics or chemistry background who are interested in using theoretical methods and computer simulations. While prior knowledge of RNA biology is not required, students should have a strong curiosity for complex systems and multidisciplinary research and be excited to work with researchers in experimental teams, which will need strong scientific communication skills. Students with a biochemistry background are welcome to apply, but will need to be interested in understanding physics-based models which are mathematical in nature.

In this PhD you will learn to use existing academic software for performing molecular dynamics simulations of polymers using High Performance Computing (HPC facilities). You will need to write your own computer code in a language of your choice (such as Python) to analyse the data. You will learn how to apply the principles of statistical physics and thermodynamics to problems in molecular biology and biochemistry, and how to build physics-based models that capture the behaviour of highly complex systems without including all of the chemical details of the molecules involved.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Using atomic force microscopy to unravel the biophysics of the cell wall of a major fungal wheat pathogen in life and death (S3.5-MPS-Hobbs)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Prof Jamie Hobbs, Dr Sam Amsbury and Prof Andrew Fleming.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

The fungal cell wall is a complex molecular material which maintains cell shape and function in the presence of the osmotic pressure caused by the high internal concentration of molecules necessary for life. Despite decades of study, we know remarkably little about how the fungal wall functions as a mechanical system. Even though failure of the wall is the ultimate reason for cell death, we don’t know how this failure actually happens, hindering strategies for tackling fungal infections. Fungi are the major food crop pathogens, with typical annual losses of 10-30%, while human fungal pathogens can have alarmingly high mortality rates of around 50% even when treated. There is a pressing societal need for new approaches for tackling fungal infection, which this project, by taking a novel physics-based approach, aims to address.

Over the last decade we have developed a suite of atomic force microscopy (AFM)-based tools for probing the architecture and physical properties of cell walls (e.g. Pasquina-Lemonche et al, 2020 Nature 582(7811), 294, Adedeji-Olulana et al 2024, Science 386, 6721, 573), focused on bacteria, and over the last four years have transferred these approaches to fungi (Colclough et al, submitting to Nature Microbiology). These tools allow us to image the cell wall down to the molecular scale for the first time and hence determine its structural organisation. As AFM is a mechanical microscope, in which the image is built up point-by-point by poking the surface with a nanometrically sharp tip, it can also be used to extract mechanical information about the sample. The aim of this project is to combine this imaging and force measuring capability to explore what components of the cell wall play what mechanical role. Where appropriate, new data analysis approaches will be developed to extract the mechanical properties of the individual fibres that constitute the cell wall, and to explore how these properties are influenced by the local environment and hence stress state within the wall. The project will then examine how the cell wall changes when cells are treated with antifungal agents, and how these changes influence the local mechanical properties. Combined, this will provide novel insights into how the cell wall behaves as a mechanical system.

The project is highly interdisciplinary, bringing together cutting-edge instrument/biological physics with fungal biology. Although primarily experimental, an interest in applying automated computational approaches to image analysis and presentation will be important, as well as a desire to engage actively across traditional disciplinary boundaries.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

Apply now

Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Vanessa Hearnden, Prof Frederik Claeyssens and Prof Helen Colley.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Pelvic fistulas occur when the vaginal wall becomes damaged, for example during prolonged, obstructed labour, and a hole develops between the vagina and the bladder or the vagina and the rectum. Leakage of urine or faeces into the vaginal canal causes debilitating symptoms including incontinence, chronic pelvic pain, abnormal vaginal discharge, and recurrent urinary tract infections. In addition to the physical symptoms, fistulas can also lead to psychological distress and social isolation. Current treatment methods rely on invasive reconstructive surgery, and recurrence rates are high resulting in high levels of morbidity.

Pelvic fistulas are more prevalent and harder to treat in low resource settings where emergency obstetric care and complex surgical reconstruction are less accessible. Because pelvic fistulas sit at the intersection of women’s health and global economic disparity, there is a clear, unmet clinical need for novel, accessible, and minimally invasive treatments with lower recurrence rates.

This project aims to adapt and refine porous polymer materials, which have been extensively studied at the University of Sheffield over the past 10 years, to design a patch which achieves the required properties to facilitate the repair of pelvic fistulas. Using polymeric high internal phase emulsion (polyHIPE) materials enables us to control the porosity, stiffness and shape of the patches and during this project you will further improve the functionality of these materials by including mucoadhesive and barrier functions. The design and development process will be informed by clinical stakeholders, and the materials will be tested biologically using tissue engineered models and ex vivo animal tissues.

This interdisciplinary project will combine biomaterials science, tissue engineering and polymer chemistry to design and test a polymeric patch, giving you the opportunity to learn new skills and techniques. You will have the opportunity to work with an experienced supervisory team with expertise in biomaterials for soft tissue regeneration, polymer chemistry and mucoadhesive patches. There will also be opportunities to engage with clinical experts and to access training in commercialisation and clinical translation.

On this project you will be a part of the postgraduate research communities in both the School of Chemical, Materials and Biological Engineering and the School of Clinical Dentistry giving you opportunities to engage with other PhD students, showcase your research, and be a part of these supportive research communities. You will also benefit from being a part of the Insigneo institute and the Women’s HealthTech Innovation Network providing access to a wider network of individuals beyond academia and opportunities to further enhance your studies.

If you have questions about the project, what it is like to be a PhD student at the University or to discuss the support and training we provide then please do get in touch.

How to 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.

Please refer to the EPSRC DLA webpage for detailed information about the EPSRC DLA and how to apply. Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Engineering a Closed-Loop Thermal Neurointerface for Brain Ischaemia (S3.5-DEN-De Felice)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Milena De Felice and Dr Thomas Paterson.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Temperature has a powerful effect on how nerve cells function and respond to injury. Lowering temperature can reduce the metabolic demand of neural tissue and alter electrical activity, which is why controlled cooling continues to attract interest as a way of limiting damage after ischaemic injury. What is less well understood is how the precise pattern of cooling experienced by different parts of a neural network influences subsequent recovery.

In most experimental studies, cooling is described using a single target temperature. In practice, however, temperature changes continuously across both space and time. Cells closest to a cooling source may cool more quickly or reach a lower temperature than cells only a short distance away. Understanding these local differences could help us define cooling conditions more precisely and make experimental studies easier to compare.

This PhD will develop a small, soft microfluidic device capable of producing controlled local cooling across cultures of cortical neurons. Temperature-controlled fluid will pass through channels positioned beneath or alongside the culture, without direct contact with the cells. Measurements from a small number of temperature sensors will be combined with a computer model of heat transfer to reconstruct how temperature changes across the neuronal network over time.

The student will then investigate whether these local thermal differences influence recovery after ischaemic stress. Cortical neuronal cultures will undergo oxygen-glucose deprivation followed by reoxygenation, an established laboratory model of ischaemic injury. Their electrical activity will be monitored using a multi-electrode array, allowing neuronal activity to be recorded simultaneously from many locations within the same culture.

This creates an unusual opportunity to relate the temperature history experienced at each part of the network to its subsequent functional recovery. After the cultures have returned to normal temperature, the student will quantify recovery of neuronal firing, bursting and coordinated network activity. Measuring recovery after rewarming is important because cooling itself can temporarily suppress electrical activity; the aim is to determine whether particular cooling conditions support better recovery rather than simply changing activity while the cells are cold.

The project will focus initially on three variables that are likely to shape the response: how far temperature is reduced, when cooling begins relative to the injury, and how long cooling is maintained. Rewarming will be standardised so that these factors can be examined clearly. Designed experiments will allow the student to identify informative combinations efficiently and determine how cooling conditions interact.

The multi-electrode-array platform and established cortical neuronal culture capability will be available within the supervisory environment, allowing the student to concentrate on developing and testing the new thermal-control approach rather than establishing major infrastructure.

The project brings together engineering and neuroscience. The student will gain experience in device design, microfluidics, temperature sensing, heat-transfer modelling, neuronal culture and electrophysiology. Rather than attempting to develop a clinical implant within the PhD, the project will establish the engineering and biological principles needed to control neural cooling more precisely. Its main outputs will be a validated experimental platform and quantitative design rules linking local thermal exposure to neuronal recovery, providing a foundation for future focal cooling and neural-interface technologies.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Swallowable Self-Assembling Robots for 3D Printing Inside the Body (S3.5-ELE-Miyashita)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Shuhei Miyashita and Dr Thomas Paterson.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Treating wounds and disease in the gastrointestinal tract often requires delivering instruments and materials through narrow, complex pathways using endoscopes or catheters. These approaches restrict the size and shape of structures that can be delivered, and insertion can cause pain and physical discomfort. What if we could manufacture what is needed directly inside the body? Protective wound patches, structures that release drugs locally, and temporary tissue supports illustrate the potential of 3D printing to support different treatments.

At the University of Sheffield’s Microrobotics Lab, you will develop an ‘inside-the-body 3D printer‘ that self-assembles from swallowable modules inside the gastrointestinal tract. Initially focusing on the colon, you will investigate engineering feasibility by assembling the printer and fabricating supports for locally kinked intestinal passages in laboratory models.

Why this matters

Wounds and surrounding tissues vary between patients and treatment sites. Manufacturing directly at the target site could allow structures to match local conditions, from patches following wound contours to supports reinforcing only the areas that need them.

Achieving this requires sufficient working space and manufacturing capability within a system small enough to swallow. Self-assembly offers a solution: modules remain small during delivery, then combine into a larger functional device.

The ambition is to give robots the ability to manufacture inside the body, extending their role beyond observation and diagnosis toward supporting tissue protection, repair, and function.

What you will do

You will address five connected challenges:

1. Magnetic printing: Develop a printer with controlled movement in three directions using externally applied magnetic fields.

2. Self-assembly: Design swallowable modules that join together into a working printer within confined spaces.

3. Design: Design and print temporary structures that counter localized intestinal kinking and maintain an open passage.

4. Materials selection: Work with collaborators to select expandable biomaterial filaments suitable for swallowing and develop modules for their use.

5. System evaluation: Measure assembly reliability, printing accuracy, and support performance in model intestines, comparing printed structures with prefabricated supports.

You will investigate how to stabilize a printer in a soft, deformable environment and provide mechanical support without unnecessarily restricting surrounding movement.

Why now, and why Sheffield?

The Sheffield Microrobotics Lab has world-leading technology for wireless magnetic actuation with two independent degrees of movement [Ref 1]. Its longstanding research in microrobotics and self-assembly is complemented by collaborations connecting expertise in biomaterials and soft materials with clinical knowledge of gastrointestinal medicine.

Building on existing prototypes, you will work across disciplines to integrate these capabilities into a swallowable, self-assembling printer for manufacturing functional structures inside the body.

Your development and future opportunities

You will gain practical experience in robotic design, fabrication, magnetic control, biomaterials, and experimental evaluation. Weekly individual supervision, group meetings, journal clubs, and support from senior students will help you develop research planning, problem-solving, and critical reading skills. Participation in Sheffield Robotics activities will connect you with researchers across disciplines. Attending and presenting at conferences and writing research papers will develop your ability to communicate and discuss findings while building national and international research connections. These technical, communication, and collaboration skills can support careers in academia, robotics, medical devices, and advanced manufacturing. Beyond gastrointestinal applications, the engineering principles could inform manufacturing and repair in other confined environments that are difficult to access.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Mechanobiology‑Driven Design of Low‑Modulus Ti Scaffolds (S3.5-MAC-Cheong)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Vee San Cheong and Prof Frederik Claeyssens.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Large defects in bone remain a challenge in Orthopaedics as they exceed the body’s capability for repair. Current metallic implants often cause stress shielding in bone due to a mismatch in modulus, whereas ceramic- and polymer-based implants lack sufficient strength for load-bearing applications. Low-modulus Ti-based scaffolds (eg. TiTa) can now be manufactured (e.g.. additive manufacturing, templating etc), and they offer a possibility for designing scaffolds with superior response at the structural level to meet mechanical demands, and at the cellular level for improved bone regeneration. However, scaffold designs often do not consider the inherent trade-offs between mechanics and biology across length-scales.

The purpose of this PhD is to use a combined experimental-computational approach to improve our understanding of how mechanical signals at different length-scales lead to specific biological response. The student will fabricate porous low modulus scaffolds and perform experimental testing to determine the cellular response. To achieve these scaffold materials we will explore direct additive manufacturing of the metal scaffolds, while porous polymer templates will be used for metallisation, to mimic in particular the structure of trabecular bone. Finite element models incorporating cell-fate rules will be developed to predict the mechanobiological response. The validated models will be used to refine and optimise the design, followed by fabrication and experimental testing.

The successful candidate will directly benefit from working on a clinically and industrially relevant topic. Moreover, the student will be part of the vibrant multi-disciplinary Integrative Musculoskeletal Biomechanics (IMSB) team, which includes academics, postdoctoral researchers and PhD students from the Insigneo Institute for Healthcare Technology.

Requirements

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

- Have a background in mechanical engineering or bioengineering.

- Proficiency in finite element analysis software (e.g., Abaqus, Ansys), and programming (e.g., Python, Matlab).

- Experience in mechanical testing and/or cellular characterisation would be an advantage.

- Strong problem-solving skills and willingness to learn.

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

Supervisory team

The student will be part of the vibrant multi-disciplinary Integrative Musculoskeletal Biomechanics (IMSB) team, which includes academics, postdoctoral researchers and PhD students from the Insigneo Institute for Healthcare Technology.

Dr Vee San Cheong (primary supervisor) is a UKRI Future Research Leaders Fellow with expertise in developing novel methods to study bone biomechanics. In collaboration with researchers and surgeons based at the Royal National Orthopaedic Hospital (RNOH) and Insigneo Institute, she has been studying bone mechanics using innovative X-ray imaging techniques and FEA in the context of bone remodelling to predict the effect of implant design, drugs and exercise on bone adaptation.

Prof Frederik Claeyssens (co-supervisor) has extensive experience and publication track record in developing novel materials for bone tissue engineering through additive manufacturing and polymer templating techniques. He has been working on in vitro, in ovo and ex vivo assessment of cell ingrowth and bone formation together with colleagues of the Insigneo institute, where he focuses on non-animal based, pre-clinical assessments.

How to 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.

Please refer to the EPSRC DLA webpage for detailed information about the EPSRC DLA and how to apply. Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

Apply now

Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Prof Claire Brockett, Prof Cheryl Miller and Prof Christine Le Maitre.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Ankle osteoarthritis affects between 1-4% of the adult population. Often associated with a prior injury - such as an ankle fracture or severe sprain - it typically affects younger patients than other types of osteoarthritis. Total ankle replacement has been a surgical treatment for nearly fifty years, as it reduces pain and preserves natural joint motion. However, ankle replacements have a much higher rate of failure than other joint replacements, and have complications such as loosening and infection within the first five years of implantation.

This 3.5 year PhD project aims to investigate novel, injectable bone substitutes as a solution to reduce these risks. Through this PhD you will undertake mechanical modelling to assess the local loading environment between an implant and the bone during the early stages of healing following surgery through development of simple experimental models to investigate the effects of surgical procedure on the interaction between bone and implant.

You will use new human tissue culture platforms to assess the remodelling of bone around ankle implants - examining conditions including complete immobilisation following surgery to gradual increase in weight-bearing and motion. Using joint tissue ethically donated from replacement surgeries, you will build "mini-joint" models in the laboratory. Initially modelling the current clinical approach (bone/implant), you will then explore the use of injectable bone-substitute materials to increase the extent and rate of remodelling, and evaluate their impact on inflammation and infection around the joint.

This multi-disciplinary project will work across the Faculties of Health and Engineering and will be based in the Insigneo Institute for Healthcare Technology. You will also benefit from alignment with a large multi-institution EPSRC programme grant that will provide access to a wider research community and UK networking activities.

Alongside the research project, you will undertake a bespoke training package including:

-Biomechanical modelling

-Advanced 3D cell and human tissue culture, including dynamic biomechanical loading.

-Biomaterial characterisation and analysis

-Research integrity and ethical practices for human tissue research

-Medical device innovation

-Patient and public engagement

Candidates: We welcome applicants with a strong background in bioengineering, tissue engineering, or a related discipline, who are passionate about translational medicine and multidisciplinary research.

Potential Impact: This project is key to improving outcomes for total joint replacement, and beyond total ankle replacement, this could be adopted as a treatment for other joint replacements - impacting thousands of patients globally. The experimental methods developed within this PhD could also be translated for development of models that support pre-clinical evaluation of new designs and materials for total joint replacement.

How to 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.

Please refer to the EPSRC DLA webpage for detailed information about the EPSRC DLA and how to apply. Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.

PhD Opportunity: Chemical and mechanical guidance in collective cell migration (S3.5-MPS-Ucar)

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Details

A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.

Supervised by Dr Mehmet Can Ucar and Dr Kyra Campbell.

The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.

Project Description

Cell migration is fundamental to life. During embryonic development, cells travel to precise locations to form tissues and organs. In the immune system, cells coordinate their movement to fight infection or infiltrate tumours. Understanding how groups of cells move collectively is therefore a question with broad implications for biology and medicine.

But how do cells "decide" which way to go? Migrating cells are guided by multiple cues that provide directional information. Chemical signals can be one source, sometimes pre-patterned in the tissue and sometimes actively generated by the cells themselves (a process known as self-generated chemotaxis). Cells can also guide one another through direct mechanical contact, pushing, pulling, and adhering to their neighbours. While each mechanism has been studied individually, most biological scenarios involve mixtures of different cell types coordinating their movement through multiple cues at once. How chemical and mechanical guidance interact in such mixed populations is a largely open question.

In recent work (Ucar et al 2025), we developed a mathematical framework describing how two distinct cell populations can collectively generate and follow chemical gradients. This successfully predicted the behaviour of mixed immune cell populations in experiments, showing that a balance between the signalling capabilities of each cell type is critical for robust collective movement. However, this framework addressed chemical signalling alone. Extending it to incorporate mechanical interactions, and understanding when these forces cooperate or compete with chemical guidance, is the central challenge this project addresses.

What you will do:

In this project, you will develop new mathematical and computational models that combine chemical and mechanical interactions between different cell types. You will:

• Build on our existing theoretical framework by incorporating mechanical forces (such as adhesion and repulsion between cells) into continuum models, and determine how they reshape collective migration across different guidance scenarios.
• Develop cell-resolved simulations that capture mechanical interactions, testing when the population-scale theory holds and when individual cell properties become important.
• Compare your model predictions against experimental data from Dr Kyra Campbell's laboratory (Sheffield Biosciences), where live imaging of mixed cell populations during fruit fly gut development provides a well-controlled system to test theoretical predictions.

This combination of mathematical analysis, simulation, and experimental comparison will provide a unified understanding of how different guidance cues act in concert to enable the robust movement of mixed cell populations.

Skills and training:

You will be based in the School of Mathematical and Physical Sciences, within the Physical Sciences of Biological Systems research cluster, and co-supervised with Dr Kyra Campbell in Biosciences. You will gain expertise in continuum partial differential equation methods, agent-based modelling, and statistical analysis of large biological trajectory datasets. No prior biology experience is required; the project is designed for students with a background in applied mathematics, physics, computational biology, or a related quantitative discipline.

The University of Sheffield offers a comprehensive doctoral development programme, including courses in academic writing, presentation skills, programming, research ethics, and time management. You will also have the opportunity to present your work at national and international conferences. The quantitative and computational skills developed during this project are highly transferable, opening pathways to careers in academia or data-oriented roles in industry.

How to 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.

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

Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.

The application deadline is 17:00 GMT 13 January 2027.

The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.