EPSRC Industrial Doctoral Landscape AwardAn EPSRC industrial doctoral landscape award between the University of Oxford and the National Physical Laboratory (NPL) is available from Oct 2025. The persistent lack of protein sequence-to-function links remains one of the most intractable problems in biology. This PhD project will focus on establishing metrology foundations for AI-driven protein design through experimentation run at scale and high throughput. The studentship is an important part of research programmes at the University of Oxford, the NPL, and the Rosalind Franklin Institute (RFI). The successful applicant will be exposed to additional collaborations with IBM, Hartree National Centre for Digital Innovation, and Wellcome Sanger Institute. The student will be involved in building an experimental capability to support predictive AI models describing relationships between DNA, protein sequences, and function. They will utilize state-of-the-art correlated imaging techniques, including electron microscopy from cryogenic to in situ, and molecular biophysics. The studentship is linked to key developments in the NPL’s newly launched synthesis bio-foundry and the RFI’s theme of correlated imaging. The technical role of NPL will complement the efforts at RFI and Oxford in developing correlated datasets for data-driven protein design and providing metrology for biophysical assessment of function. The student will have access to metrology training and NPL capabilities throughout their studies, spending time at Oxford, NPL, and RFI. They will also collaborate with scientists at RFI working on advanced imaging instruments using optical, X-ray photons, and electrons. Minimum RequirementsEssential: Prior degree (1st or 2.1) in biochemistry, chemistry, or physics. Desirable: Experience in AI, microscopy, and synthetic biology. For more information, visit:
Job Title
PhD Vacancy - AI-driven metrology for sequence-to-function links with accuracy of one DNA base pair