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 Jefersson A. dos Santos and Dr Robert Bryant.
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 is increasingly being used to analyse images, from monitoring changes in the natural environment to supporting medical diagnosis and analysing images from scientific experiments. Recent advances have led to the development of Foundation Models: large artificial intelligence models trained on enormous collections of data that can be adapted to perform many different tasks. These models have demonstrated impressive capabilities, but their size and complexity can make them expensive to operate, difficult to deploy on limited hardware, and challenging for people to understand and trust.
This project aims to develop new methods for creating smaller, more efficient and more interpretable models from these large artificial intelligence systems. The research will investigate a process known as knowledge distillation, in which a smaller model learns from a larger, more capable model. Rather than simply copying the predictions of the larger model, this project will investigate how important scientific knowledge and known physical principles can be incorporated into this process. It will also explore ways of representing the resulting knowledge using simpler, more understandable mathematical or symbolic models.
The central goal is to determine whether the capabilities of large artificial intelligence models can be transferred to much smaller models while retaining useful performance and improving transparency, efficiency and reliability. This could make advanced artificial intelligence more practical in situations where computing resources are limited or where users need to understand why a system has reached a particular conclusion.
The research will be evaluated using real-world image analysis problems, with potential applications including environmental monitoring, medical imaging and microscopy. These areas are particularly relevant because they often involve complex scientific data while requiring models that are reliable, efficient and understandable to domain experts.
The project is timely because artificial intelligence models are becoming increasingly powerful and widely adopted, while concerns about their computational cost, transparency and reliability are growing. By combining large-scale artificial intelligence with scientific knowledge and interpretable modelling, this research aims to develop a bridge between powerful but complex artificial intelligence systems and smaller models that can be more readily understood, deployed and used in scientific and practical settings.
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.
