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PhD Studentship in Physics-Guided Representation Learning for Drug Discovery University of Cambridge in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Four-year funded PhD studentship covering a maintenance stipend and University tuition fees at the UK/Home rate. Open to candidates eligible for Home-fee status; international candidates may be considered if additional funding is secured to cover the difference between Home and international tuition fees, which cannot be self-funded.
Deadline
Jan 6, 2027
Country
United Kingdom
University
University of Cambridge

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About this position
AstraZeneca-funded PhD studentship at the University of Cambridge in the Department of Pure Mathematics and Mathematical Statistics.
The project, Physics-Guided Representation Learning from Free Energy Perturbation Data for Affinity Prediction and Candidate Down-Selection, sits at the interface of statistical machine learning, molecular simulation, drug discovery, and computational chemistry. The successful student will work on developing new machine-learning methods for predicting molecular binding affinities and improving decision-making in pharmaceutical research, using both public benchmark datasets and AstraZeneca’s industrial expertise and data.
Supervision is joint between Prof Sergio Bacallado (University of Cambridge), Dr Dino Oglic (AstraZeneca), and Dr Jon Paul Janet (AstraZeneca).
This is a four-year funded PhD studentship. Funding covers a maintenance stipend and University tuition fees at the UK/Home rate. The opportunity is open to candidates eligible for Home-fee status; international candidates may be considered if extra funding is secured to cover the fee difference.
Applicants should have, or expect to obtain, a first-class or upper second-class honours degree (or equivalent), normally after four years of university study. Suitable backgrounds include Mathematics, Statistics, Machine Learning, Scientific Computing, or related disciplines. Experience with computational chemistry is desirable but not essential.
Apply via the University of Cambridge Postgraduate Applicant Portal for PhD in Pure Mathematics and Mathematical Statistics. Indicate Dr Sergio Bacallado as proposed supervisor and use the project title provided in the advert. Required documents include transcripts, CV, two references, and the relevant research summary and reasons for applying.
Deadline: 6 January 2027 at 23:59 UK time. Shortlisted candidates will be interviewed in January 2027.
Funding details
Four-year funded PhD studentship covering a maintenance stipend and University tuition fees at the UK/Home rate. Open to candidates eligible for Home-fee status; international candidates may be considered if additional funding is secured to cover the difference between Home and international tuition fees, which cannot be self-funded.
What's required
Applicants should have, or expect to obtain, a first-class or upper second-class honours degree (or equivalent), normally following four years of university study. Suitable backgrounds include Mathematics, Statistics, Machine Learning, Scientific Computing, or closely related disciplines. Experience with computational chemistry is desirable but not essential; strong mathematical or machine-learning backgrounds are particularly welcome.
How to apply
Apply through the University of Cambridge Postgraduate Applicant Portal for PhD in Pure Mathematics and Mathematical Statistics. Indicate Dr Sergio Bacallado as proposed supervisor and use the specified project title. Submit transcripts, CV, two references, and the required research summary and reasons for applying.
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