PhD studentship: Generative Modelling for Foundational Discovery in Biomedicine
CompanyUniversity of Cambridge
Location
Cambridge, England, United Kingdom
Employment TypeFull-time
Posted At5/28/2026
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Description
Supervisor: Dr Hana Aliee
Department/Location: Cancer Research UK Cambridge Institute
Deadline for application: 8th June 2026
Course start date: January 2027
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 https://www.cruk.cam.ac.uk/research-groups/aliee-group/
In the Aliee Group, 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 reasoning in high-dimensional multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery.
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 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
Department/Location: Cancer Research UK Cambridge Institute
Deadline for application: 8th June 2026
Course start date: January 2027
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 https://www.cruk.cam.ac.uk/research-groups/aliee-group/
In the Aliee Group, 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 reasoning in high-dimensional multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery.
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 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 £22,500pa in 2026-27, 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:
https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcrpdmsc
You should select to commence study in Lent Term (January) 2027.
Additional information
To complete your online application, you will need to answer/provide the following:
Choice of project and supervisor
Please ensure that you name the project (with reference code) and supervisor, where indicated.
Course-specific questions
~ You will be asked to give details of your Research Experience (up to 2,500 characters)
~ Your Statement of Interest (up to 2,500 characters) should explain why you wish to be considered for the studentship and what qualities and experience you will bring to the role.
Supporting documents
Applicants Will Be Asked To Provide
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 £22,500pa in 2026-27, 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:
https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcrpdmsc
You should select to commence study in Lent Term (January) 2027.
Additional information
To complete your online application, you will need to answer/provide the following:
Choice of project and supervisor
Please ensure that you name the project (with reference code) and supervisor, where indicated.
Course-specific questions
~ You will be asked to give details of your Research Experience (up to 2,500 characters)
~ Your Statement of Interest (up to 2,500 characters) should explain why you wish to be considered for the studentship and what qualities and experience you will bring to the role.
Supporting documents
Applicants Will Be Asked To Provide
~ Academic transcripts
~ Evidence of competence in English (if appropriate)
~ Details of two academic referees
~ CV/resume
Deadline
The closing date for applications is 8th June 2026, with interviews expected to take place in June/July 2026.
Please quote reference SW49526 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.
~ Evidence of competence in English (if appropriate)
~ Details of two academic referees
~ CV/resume
Deadline
The closing date for applications is 8th June 2026, with interviews expected to take place in June/July 2026.
Please quote reference SW49526 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.