PhD Studentship in Advanced AI for Perioperative Risk Stratification (AI-PREP)

Ref Number
B02\-10589
Professional Expertise
Research and Research Support
Department
School of Life \& Medical Sciences (B02\)
Location
London
Working Pattern
Full time
Salary
See advert text
Contract Type
Fixed\-term
Working Type
Hybrid
Available for Secondment
No
Closing Date
02\-Jun\-2026
About us
------------

UCL Centre for Perioperative Medicine \& UCL Hawkes Institute Funded British Journal of Anaesthesia/Royal College of Anaesthetists Non\-Clinical PhD Studentship (3 years, London)

Applications are invited for a fully funded PhD studentship at University College London (UCL) to develop next\-generation artificial intelligence tools for perioperative risk prediction and shared decision\-making. This interdisciplinary PhD sits at the interface of clinical medicine, machine learning, medical imaging, and large language models (LLMs), and will be jointly supervised by: • Dr John Whittle – Associate Professor of Perioperative Medicine, UCL • Dr Evangelos Mazomenos – Associate Professor of Medical Robotics \& AI, UCL The student will be embedded within the UCL Hawkes Institute and the UCL Centre for Perioperative Medicine, working in close collaboration with University College London Hospitals.

About the role
------------------

Project Overview Major surgery carries substantial risk. Current preoperative assessment tools are often static, resource\-intensive, and variably accessible. This PhD will develop and validate a multimodal AI framework that integrates:

• Preoperative CT imaging


• Physiological and cardiopulmonary metrics


• Structured electronic health record data The goal is to build a multimodal predictive pipelines based on letese generation deep learning methodologies, capable of dynamic perioperative risk stratification.

A central innovation is the integration of Large Language Models to translate complex model outputs into:

• Clinician\-facing interpretability summaries


• Patient\-facing explanations to support shared decision\-making This project builds on the supervisory team’s published work in:


• Automated CT segmentation (e.g. KEVS) •


• Vision transformer architectures and efficient attention mechanisms


• Machine learning risk prediction in perioperative populations

The resulting tool will form the foundation for future prospective NHS implementation studies.

Research Environment

The student will benefit from:

• Access to large\-scale, high\-quality multimodal NHS datasets


• High\-performance computing infrastructure at UCL


• Collaboration with senior NHS clinical data scientists


• Exposure to ongoing translational and AI research programmes

The project sits within a vibrant ecosystem spanning:

• Clinical AI and health informatics


• Biomedical image computing


• Translational perioperative medicine

The supervisory team has a strong record of high\-impact publication, successful doctoral supervision, and national guideline involvement.

Funding

This is a fully funded 3\-year studentship (BJA/RCoA):

• Stipend at standard UCL rate rate


• Full UCL home PhD tuition fees


• £10,000 research costs (non\-travel)


• Start date October 2026

About you
-------------

Candidate Profile We are seeking an exceptional and motivated candidate with a strong quantitative background.

Essential:

• First\-class or high 2:1 degree (or equivalent) in Computer Science, Engineering, Mathematics, Physics, Data Science, or related discipline


• Strong programming skills (Python essential)


• Experience with machine learning frameworks (e.g. PyTorch, TensorFlow)


• Demonstrable interest in healthcare AI

Desirable:

• Experience in deep learning and particularly in transformer\-based architectures


• Experience in multimodal data fusion


• Knowledge of medical imaging or health data


• Interest in explainable AI or LLM systems

Academic Output

The student will be expected to:

• Publish high quality papers in leading journals (e.g. Anaesthesia, British Journal of Anaesthesia, medical AI journals) •


• Present at international conferences in perioperative medicine and medical AI (e.g. MICCAI, IARS)


• Develop expertise in translational clinical AI suitable for future academic or industry leadership roles.

What we offer
-----------------

Equality, Diversity and Inclusion

We particularly welcome applications from candidates from underrepresented backgrounds in AI and biomedical engineering. Flexible working patterns can be discussed.

How to Apply Applicants should submit:

• CV


• Cover letter outlining research interests and relevant experience


• Academic transcripts


• Contact details for two referees Informal enquiries are encouraged and may be directed to: Dr John Whittle – j.whittle@ucl.ac.uk

https://www.ucl.ac.uk/prospective\-students/graduate/research\-degrees/division\-surgery\-and\-interventional\-science\-mphil\-phd

Our commitment to Equality, Diversity and Inclusion
-------------------------------------------------------

As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce.

These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI\+ people; and for our Grade 9 and 10 roles, women. Our department holds an Athena SWAN Silver award, in recognition of our commitment and demonstrable impact in advancing gender equality.

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