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

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 and 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.

Project Overview: Major surgery carries substantial risk. Current pre‑operative assessment tools are often static, resource‑intensive, and variably accessible. This PhD will develop and validate a multimodal AI framework that integrates physiological and cardiopulmonary metrics, structured electronic health record data, and other modalities to build multimodal predictive pipelines based on large‑language‑model generation deep‑learning methodologies, capable of dynamic peri‑operative risk stratification. A central innovation is the integration of large language models to translate complex model outputs into clinician‑facing interpretability summaries and patient‑facing explanations to support shared decision‑making. The project builds on the supervisory team’s published work in vision transformer architectures, efficient attention mechanisms, and machine‑learning risk prediction in peri‑operative populations. The resulting tool will form the foundation for future prospective NHS implementation studies.

Research Environment

• 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

Funding: This is a fully funded 3‑year studentship (BJA/RCoA). Standard UCL stipend rate.

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

• First‑class or high 2:1 degree (or equivalent) in Computer Science, Engineering, Mathematics, Physics, Data Science, or a related discipline
• Experience with machine‑learning frameworks such as PyTorch or TensorFlow
• Demonstrable interest in healthcare AI
• 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 such as Anaesthesia, the British Journal of Anaesthesia, and medical AI journals
• Present at international conferences in perioperative medicine and medical AI such as MICCAI and IARS
• Develop expertise in translational clinical AI suitable for future academic or industry leadership roles

Equality, Diversity and Inclusion: We particularly welcome applications from candidates from underrepresented backgrounds in AI and biomedical engineering. Flexible working patterns can be discussed. 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, including 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.