PhD Studentship – Generative Modelling for Foundational Discovery in Biomedicine

RFCSR; Cambridge; On-site; GBP 18,000 - 21,000; Full time

Benefits offered by this job

Tuition fees covered

Doctoral training

Job summary

The University of Cambridge invites applications for a fully funded PhD studentship focused on generative modelling for foundational discovery in biomedicine. This research opportunity blends machine learning, computational modelling, and biomedical science to advance understanding of complex biological systems and accelerate scientific discovery.

The successful candidate will work within a collaborative research environment at the intersection of artificial intelligence and biomedical research,

Qualifications

• Strong foundation in machine learning, statistical modelling, or computational methods.
• Programming experience with Python and data science tools.
• Ability to work independently and in multidisciplinary teams.

Responsibilities

• Design novel generative modelling approaches for biomedical data.
• Implement machine learning models and assess their scientific value.
• Contribute to high-impact research outputs and publications.

Skills

Machine learning

Statistical modelling

Computational methods

Python

Analytical thinking

Communication skills

Education

Bachelor's or equivalent in CS/Math/Physics/Engineering/Computational biology

Master's degree with substantial research

Tools

Python

Job description

PhD Studentship – Generative Modelling for Foundational Discovery in Biomedicine

University of Cambridge
Cambridge, United Kingdom

The University of Cambridge invites applications for a fully funded PhD studentship focused on generative modelling for foundational discovery in biomedicine. This research opportunity is part of a cutting-edge interdisciplinary programme that combines machine learning, computational modelling, and biomedical science to advance understanding of complex biological systems and accelerate scientific discovery.

The successful candidate will work within a highly collaborative research environment at the intersection of artificial intelligence and biomedical research. The project aims to develop and apply advanced generative modelling techniques to biomedical data, enabling new insights into disease mechanisms, biological processes, and therapeutic development. The role involves designing novel computational approaches, implementing machine learning models, and contributing to high-impact research outputs including peer-reviewed publications.

The studentship provides access to world-class research infrastructure, including advanced computing resources and close collaboration with leading experts in machine learning, computational biology, and medical sciences. The candidate will also benefit from structured doctoral training, interdisciplinary collaboration, and opportunities to engage with both academic and industry partners.

Eligibility Criteria
Applicants should hold, or expect to obtain, a strong undergraduate degree in a relevant discipline such as computer science, mathematics, physics, engineering, computational biology, or a closely related field. A master’s degree with a substantial research component is desirable. Candidates must meet the University of Cambridge entry requirements for doctoral study.

Required Expertise/Skills
Applicants should demonstrate a strong foundation in machine learning, statistical modelling, or computational methods. Experience with programming languages commonly used in data science and machine learning (such as Python) is essential. Strong analytical and problem-solving abilities are required, along with an interest in biomedical applications. Candidates should also demonstrate the ability to work independently and collaboratively in a multidisciplinary research environment, with strong written and verbal communication skills.

Salary Details
The studentship is fully funded and includes coverage of University tuition fees and a maintenance stipend at the standard UKRI doctoral rate, subject to eligibility and funding conditions. Additional research support may be available depending on the project structure.

Application Deadline
Refer to the official University of Cambridge recruitment page for specific closing dates associated with this studentship.