About the Project
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.
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.
