Project Overview
Modern life sciences increasingly rely on the ability to observe molecular events inside living cells. Many interactions take place at nanometre scales, far beyond the limits of conventional optical microscopes. Although advanced super-resolution microscopy techniques exist, they often require intense illumination that damages cells or limits real-time imaging.
This PhD project aims to address these barriers by developing AI-driven methods for fluorescence fluctuation super-resolution microscopy (FF-SRM), with a particular focus on the emerging Super-Resolution Radial Fluctuations (SRRF) approach. The objective is to build machine-learning models that exploit the underlying physics of fluorescence fluctuations to deliver high-resolution, low-toxicity, real-time imaging of living cells.
Your work will involve designing physics-informed AI architectures, developing spatio-temporal deep-learning models, generating realistic synthetic microscopy datasets, and validating algorithms using state-of-the-art imaging platforms at STFC’s Central Laser Facility. The research has the potential to transform how scientists study cancer, neurological disorders, metabolic diseases, and drug mechanisms in live cellular systems.
Training & Environment
This studentship provides a rare opportunity to work across two complementary environments. At UoW’s School of Computer Science and Engineering, you will join a strong research community with expertise in deep learning, imaging, and scientific computing, alongside access to extensive training and academic networks. At RAL, one of the UK’s world-class national laboratories, you will work with advanced super-resolution microscopy facilities (with Prof. Lin Wang) and benefit from immersion in multidisciplinary research, high-performance computing, and direct interactions with experts in AI for Science, computational imaging, and large-scale experimental methods (with Prof. Jeyan Thiyagalingam).
You will spend time at both sites (75% STFC:25% UoW split), gaining experience in algorithm development, experimental validation, software engineering, and scientific communication. Training opportunities include international workshops, machine-learning schools, and participation in the AI for Science programme at RAL.
Eligibility
The position is open to all applicants, but funding is available only to UK/Home candidates. Applicants should hold (or expect to achieve) a first-class or upper-second-class degree in Computer Science, Mathematics, Physics, Engineering, or a related discipline. Interest in AI, scientific imaging, computational biology, or biomedical engineering is highly desirable.
Funding
This fully funded three-year studentship is supported by the University of Westminster, the Ada Lovelace Centre of STFC Scientific Computing, and the Central Laser Facility. Funding covers full home tuition fees, a tax-free stipend starting at £23,383 (including London weighting), up to £2,000 per year for travel and subsistence, conference support, and access to additional training.
How to Apply
It is essential you include the title of the studentship in your application i.e. ‘STFC-UoW Studentship’.
Application deadline: 30th March, 2026 (for a start date of 1st October 2026).
For enquiries, please contact:
