Cambridge–AstraZeneca PhD: Physics-Guided AI for Drug Discovery (2027)
University of Cambridge
Four-year Cambridge–AstraZeneca PhD starting October 2027 to develop physics-guided machine learning for drug binding prediction; home-fee tuition and stipend are funded.
This project in Cambridge’s Department of Pure Mathematics and Mathematical Statistics uses free-energy perturbation data and experimental measurements to improve predictions of molecular binding affinity. The student will work with Professor Sergio Bacallado and AstraZeneca researchers Dino Oglic and Jon Paul Janet. It combines statistical machine learning, molecular simulation and industrial drug-discovery data.
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Benefits
Four years of maintenance stipend and Cambridge tuition at the UK/Home rate. International tuition above the Home rate is not included; overseas applicants need approved additional funding and cannot self-fund the difference. Joint academic and AstraZeneca supervision.
Eligibility summary
Applicants need a first- or upper-second-class honours degree, or equivalent, normally following four years of university study, in mathematics, statistics, machine learning, scientific computing or a related field. Home-fee candidates are eligible for the stated funding. International candidates may be considered only if an approved scholarship or other scheme covers the gap between Home and international tuition; student self-funding cannot cover that gap.
Requirements
Apply to the Cambridge PhD in Pure Mathematics and Mathematical Statistics; name Sergio Bacallado as proposed supervisor and use the official project title. Deadline: 6 January 2027, 23:59 UK time. Computational chemistry experience is helpful but not essential.
Documents required
Academic transcripts, CV, two references, research summary and reasons for applying, plus other Cambridge portal requirements.
Language/test requirements
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