AstraZeneca资助的物理引导表征学习用于药物发现方向博士研究员

Applications are invited for a four-year, AstraZeneca-funded PhD studentship in the Department of Pure Mathematics and Mathematical Statistics, commencing in October 2027.

The successful candidate will be jointly supervised by Prof Sergio Bacallado (University of Cambridge), Dr Dino Oglic (AstraZeneca), and Dr Jon Paul Janet (AstraZeneca). The studentship offers an opportunity to conduct cutting-edge research at the interface of statistical machine learning, molecular simulation and drug discovery.

Project description

The project, entitled Physics-Guided Representation Learning from Free Energy Perturbation Data for Affinity Prediction and Candidate Down-Selection, aims to develop novel machine-learning methods for predicting molecular binding affinities and supporting decision-making in drug discovery.

Free energy perturbation (FEP) calculations provide valuable information about how strongly candidate drug molecules bind to their biological targets, but these calculations are computationally expensive. This project will investigate how machine-learning models can extract richer information from FEP data and experimental measurements, combining physical principles with modern representation-learning techniques.

The research will involve developing and evaluating new statistical and machine-learning methods, with access to both public benchmark datasets and AstraZeneca's industrial drug-discovery expertise and data.

Candidate requirements

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. We welcome applicants with backgrounds in Mathematics, Statistics, Machine Learning, Scientific Computing, or any closely related discipline.

Experience with computational chemistry would be desirable but is not essential. We particularly welcome applications from candidates with strong mathematical or machine-learning backgrounds who are interested in applying their expertise to problems in drug discovery.

Funding

The studentship provides funding for four years, covering a maintenance stipend and University tuition fees at the UK/Home rate.

The studentship is open to candidates eligible for Home-fee status. International candidates may also be considered, provided that additional funding can be secured through scholarships or other approved funding schemes to cover the difference between Home and international tuition fees. This difference cannot be covered through student self-funding.

Training and supervision

The student will benefit from joint academic and industrial supervision, participating in the research activities of the Department of Pure Mathematics and Mathematical Statistics and collaborating closely with AstraZeneca researchers to gain exposure to industrial applications of artificial intelligence in pharmaceutical development.

How to apply

Applications must be submitted through the University of Cambridge Postgraduate Applicant Portal, selecting the course PhD in Pure Mathematics and Mathematical Statistics:

https://www.postgraduate.study.cam.ac.uk/courses/directory/mapmpdpms/apply

Applicants should indicate Dr Sergio Bacallado as their proposed supervisor and use the following project title:

Physics-Guided Representation Learning from Free Energy Perturbation Data for Affinity Prediction and Candidate Down-Selection.

Applications should include the supporting documentation required by the University, including academic transcripts, a CV, two references, and the relevant research summary and reasons for applying.

The deadline for applications is 6 January 2027 (23:59 UK time).

Shortlisted candidates will be invited to interview in January 2027.

Informal enquiries about the project are welcome and may be addressed to Dr Sergio Bacallado at sb2116@cam.ac.uk.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.