"PhD Studentship in Generative Modelling for Foundational Discovery in Biomedicine"

Overview

Dr. Hana Aliee wishes to recruit a student to work on the project entitled: Generative Modelling for Foundational Discovery in Biomedicine.

For further information about the research group, please visit our website at www.cruk.cam.ac.uk/research-groups/aliee-group

In the Aliee lab, we aim to address some fundamental questions in biomedicine through advancing machine learning. We develop models that represent and reason about complex biological systems, enabling predictions and interventions that can alter system behaviour in desired ways. For example, why do cells respond differently to stimuli? Why do patients vary in their responses to treatment? And what if the genome is altered, how would outcomes change?

Central to this work is the development of probabilistic generative models and counterfactual reasoning frameworks that uncover latent mechanisms and enable principled hypothesis testing. Our goal is to advance the theory of representation learning and causal inference in high-dimensional multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery.

Project details

This PhD project will contribute to the development of generative models for multimodal data representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation and inductive biases, and incorporating prior biological knowledge to enhance their predictive and explanatory power.

To address these challenges, we will work across a broad range of machine learning areas, including generative modelling (e.g. diffusion models, flow matching, self-supervised and autoregressive approaches), causal machine learning, graph neural networks, dynamical systems modelling (e.g. neural ODEs and SDEs), identifiability and interpretability, large language and sequence models, and multimodal data integration.

This position will be based at the world-leading CRUK Cambridge Institute, with close ties to the Department of Computer Science and Technology.

Preferred skills/knowledge

We are seeking a passionate and collaborative PhD student with a strong background in machine learning, solid programming skills, and a keen interest in applying their research to problems in biology and medicine to join our interdisciplinary team. Prior knowledge of biology or medicine is not necessary.

Applicants will have a BSc degree/MSc in Computer Science, Mathematics, or a similar computational field.

Funding

This four-year studentship is funded by Cancer Research UK Cambridge Institute and includes full funding for University fees and an index-linked stipend starting at £22,113 for four years.

Eligibility

We welcome applications from both UK and overseas students.

Applications are invited from recent graduates or final-year undergraduates who hold or expect to gain a First/Upper Second Class degree (or equivalent) in a relevant subject from any recognised university worldwide.

Applicants with relevant research experience, gained through Master's study or while working in a laboratory, are strongly encouraged to apply.

How to apply

Please apply via the University Applicant Portal. For further information about the course and to access the Applicant Portal, visit:

www.postgraduate.study.cam.ac.uk/courses/directory/cvcrpdmsc

You should select to commence study in April 2026.

Deadline

The closing date for applications is 15th December 2025 with interviews expected to take place in the week beginning 19th January 2026.

Please quote reference SW47896 on your application and in any correspondence about this vacancy.

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

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.