Overview
Lecturer in Health Data Science – Strand, London, WC2R 2LS
About us:
Faculty of Life Sciences & Medicine
The Faculty of Life Sciences & Medicine (FoLSM) is one of the UK’s largest and most successful centres for biomedical research and education, ranked 9th globally in the QS World University Rankings 2026. We bring together internationally leading strengths in basic, translational and clinical science, advancing human health through world‑class research and education that benefit communities locally, nationally and globally.
Our work focuses on prevention, prediction, diagnosis and management of health conditions, with growing emphasis on experimental medicine, advanced therapies, digital health and AI. Collaboration is central to our mission, with interdisciplinary teams working across the Faculty to maximise collective expertise.
FoLSM employs more than 2,000 staff across six Schools and the cross‑cutting Centre for Education. We are committed to building an equitable, inclusive and diverse community, hold a Silver Athena Swan award and contribute to equality programmes across King’s.
Based across four campuses (Guy’s, St Thomas’, Waterloo and Denmark Hill) we work closely with our NHS Hospital Trust partners under the King’s Health Partners Umbrella. Our 8,500 students study programmes spanning Medicine, Pharmacy, Nutrition & Dietetics, Physiotherapy and biomedical sciences. Our research excellence is reflected in REF2021, where 94.1% of submissions were rated world‑leading or internationally excellent. Our research is supported by strong national and international partnerships, including with the Francis Crick Institute.
School of Life Course & Population Health Sciences
The School of Life Course & Population Sciences is one of six Schools that make up the Faculty of Life Sciences & Medicine at King’s College London. The School unites over 500 experts in a wide range of population health sciences including women and children’s health, life course sciences, nutritional sciences, Health AI, preventive medicine and the molecular genetics of human disease. Our research links the causes of common health problems to life’s landmark stages, treating life, disease and healthcare as a continuum. We are interdisciplinary by nature and this innovative approach works: 91 per cent of our research submitted to the Subjects Allied to Medicine (Pharmacy, Nutritional Sciences and Women’s Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research scientists. Based across King’s Denmark Hill, Guy’s, St Thomas’s and Waterloo campuses, our academic programme of teaching, research and clinical practice is embedded across eight Departments.
About the role:
The post of Lecturer in Health Data Science will undertake research in health data science and applied artificial intelligence, contributing to King’s strategic strengths in AI-enabled health research, Learning Health Systems, trustworthy AI and the use of large-scale health data.
The post is on an Education and Research academic contract, with a balanced portfolio of education and research contributions and an increasing level of academic leadership and citizenship. Their objectives, probation reviews and subsequent annual performance development reviews will be set in accordance with the univeristy’s academic performance framework and the Faculty’s associated criteria.
All Education & Research academic staff are expected to dedicate at least 40% FTE (pro rata) of their time to education. The post holder will contribute to high-quality undergraduate and postgraduate education, supporting student learning, curriculum development and the continued growth of the Department’s health data science and AI education portfolio, including supporting the development of the new MSc in Artificial Intelligence for Health as Programme Director.
The post holder’s research is expected to be nationally recognised with a growing international profile. They are expected to build a portfolio of research grant income, outputs and impact, and to develop a research group.
Working within the Applied AI in Health group in the Department of Primary Care & Data Science, they will develop innovative approaches to address important healthcare challenges and strengthen interdisciplinary collaborations across King’s Health Partners, the NHS and other external partners.
This is a full time post (35 hours per week), and you will be offered an indefinite contract.
About you:
To be successful in this role, we are looking for candidates to have the following skills and experience:
Essential criteria
- PhD in Health Data Science, Computer Science, Artificial Intelligence, Statistics, Informatics or a related discipline.
- Evidence of an emerging independent and national research profile in health data science, AI or a related field.
- Track record of high-quality research outputs appropriate to career stage.
- Evidence of engagement with research funding activity appropriate to career stage, such as grant applications, fellowships or participation in funded projects.
- Expertise in AI and/or machine learning applied to health data.
- Experience of curriculum development and delivering high-quality and innovative teaching and assessment in higher education.
- Experience of supervising student research projects.
- Strong communication, organisational and collaborative skills, with the ability to work effectively in a multidisciplinary environment.
Desirable criteria
- Experience of working with electronic health records and large-scale health datasets.
- Experience of teaching AI or Health Data Science modules.
- Professional education qualification such as Fellowship of HEA.
- Experience of federated analytics, Learning Health Systems and/or trustworthy AI approaches.
- Experience of working collaboratively with NHS organisations, King’s Health Partners or industry.
Downloading a copy of our Job Description
Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process.
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