PhD in Physics-Guided ML for Drug Discovery (FEP)

University of Cambridge

Cambridge

On-site

GBP 19,000 - 20,000

Full time

2 days ago
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Benefits offered by this job

Stipend provided
Tuition fees at UK/Home rate
International candidates eligible with
AstraZeneca collaboration

Job summary

The University of Cambridge invites applications for a four-year AstraZeneca-funded PhD studentship in the Department of Pure Mathematics and Mathematical Statistics, starting October 2027. The successful candidate will be jointly supervised by Prof Sergio Bacallado, Dr Dino Oglic and Dr Jon Paul Janet.

The project, Physics-Guided Representation Learning from Free Energy Perturbation Data for Affinity Prediction and Candidate Down-Selection, will develop ML methods to predict molecular binding

Qualifications

  • A first-class or upper second-class honours degree (or equivalent) in Mathematics, Statistics, ML, or Scientific Computing.
  • Strong mathematical and statistical background with ML experience.
  • Interest in applying expertise to drug discovery and computational chemistry is desirable.

Responsibilities

  • Develop ML methods for representing FEP data and predicting molecular binding affinities.
  • Collaborate with academia and AstraZeneca researchers on project-related studies.
  • Prepare research summaries, results, and contribute to publications.

Skills

Mathematics
Statistics
Machine Learning
Scientific Computing
Computational Chemistry (desirable)

Education

Honours degree in Mathematics/Statistics/ML/Scientific Computing

Job description

The University of Cambridge invites applications for a four-year AstraZeneca-funded PhD studentship in the Department of Pure Mathematics and Mathematical Statistics, starting October 2027. The successful candidate will be jointly supervised by Prof Sergio Bacallado, Dr Dino Oglic and Dr Jon Paul Janet.

The project, Physics-Guided Representation Learning from Free Energy Perturbation Data for Affinity Prediction and Candidate Down-Selection, will develop ML methods to predict molecular binding

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