4-year PhD fellowship in Deep Learning-Accelerated Crystallography Pipeline Durham University in United Kingdom

[Stipend covers the standard UK salary and all university/institutional fees, with a budget for travel, conferences and equipment.]

Durham University is offering a 4-year PhD fellowship in the research programme Deep Learning-Accelerated Crystallography Pipeline, focused on mathematical and machine learning aspects in crystallography. The project sits within a Novo Nordisk Foundation-funded collaboration involving Durham University, the University of Copenhagen, and the MAX IV synchrotron, with opportunities to work in an international environment alongside mathematicians, crystallographers, and data scientists.

The PhD will be based in the Department of Mathematical Sciences at Durham University and aims to advance small-molecule structure determination by developing new mathematical methods and integrating machine learning into crystallographic workflows. The successful candidate will work on theoretical and computational approaches to improve structure solution, refinement, and validation, with the chance to interact with OlexSys and collaborators at the University of Copenhagen and MAX IV in Lund.

Supervision is provided by Professor Norbert Peyerimhoff (Principal Supervisor) and Dr Niklas Ruth (Co-Supervisor). Their combined expertise spans mathematics, modern crystallography, and machine learning. The role is suited to a student who wants to combine rigorous theory with coding and applied research, and who is interested in seminar participation, research visits, workshops, conferences, and thesis writing as part of a structured doctoral programme.

Eligibility is broad in academic background, but applicants should hold, by the start of the PhD, a qualification equivalent to a Master’s degree in Chemistry, Mathematics, or Computer Science. The advert also highlights a strong background in quantum crystallography and its mathematical foundations, solid programming skills, and willingness to apply machine learning where appropriate. Experience contributing to open-source scientific software is optional.

Funding is available for the full four-year fixed-term position. The stipend covers the standard UK salary and all university/institutional fees, and also includes a budget for travel, conferences, and equipment. The PhD is scheduled to commence on 1 January 2027 or as soon as possible thereafter.

Applications should be submitted as a single PDF including a CV, a cover letter describing motivation, a certified copy of the Master’s diploma and transcript, and the names, institutions, positions, and email addresses of up to two referees. If the degree is not yet completed, a certified/signed recent transcript or a written statement from the institution or supervisor may be provided instead. Applications must be emailed to the supervisors by 7 October 2026 at 23:59 BST.