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Ben Glocker

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Fully Funded PhD in Counterfactual Image Generation in Healthcare at Imperial College London Imperial College London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded for Home (UK) students, covering tuition and a tax-free stipend for 3.5–4 years, plus conference travel support (e.g. MICCAI, NeurIPS).

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Country

United Kingdom

University

Imperial College London

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Keywords

Computer Science
Biomedical Engineering
Electrical Engineering
Medical Imaging
Deep Learning
Mathematics
Artificial Intelligence
Medical Science
Insurance
Generative Modeling
Physics
Large Language Models
ML

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About this position

Imperial College London is advertising a fully funded PhD studentship in BioMedIA / Team MIRA within Imperial College Computing, as part of the UKRI AI for Healthcare Centres (AI4Health) and co-funded by GSK.

The project focuses on counterfactual image generation in healthcare, with an emphasis on developing concept-driven generative models that work without explicit causal annotations. The research will explore how to leverage large language models and vision models to build concept dictionaries for medical imaging and to develop the next generation of causal generative foundation models.

The studentship offers hands-on work with large-scale open datasets and supervision from an experienced team including Ben Glocker, with industry collaborators Jessica Schrouff and Xiaodan Xing.

Applicants should have a background in Computer Science, Machine Learning, or AI, with strong deep learning and Python skills. Backgrounds in engineering, mathematics, or physics with strong data-analysis skills are also welcome. Prior medical imaging experience is a plus but not required.

Funding is fully funded for Home (UK) students and includes tuition fees, a tax-free stipend for 3.5–4 years, and conference travel support (e.g. MICCAI, NeurIPS).

There is no explicit deadline stated in the post; the position is advertised as starting as soon as possible. Interested candidates should review the programme details and apply via the AI4Health application portal.

Funding details

Fully funded for Home (UK) students, covering tuition and a tax-free stipend for 3.5–4 years, plus conference travel support (e.g. MICCAI, NeurIPS).

What's required

Motivated researcher with a background in Computer Science, Machine Learning, or Artificial Intelligence, and solid deep learning and Python skills. Backgrounds in engineering, maths, or physics with strong data-analysis skills are also welcome. Prior medical imaging experience is a plus but not required. The funding is for Home (UK) students.

How to apply

Review the programme details at ai4health.io/training, then submit an application through ai4health.io/apply. For application-process questions, email [email protected]; for informal project queries, email [email protected].

More information can be found here

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