King's College London

About Us

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 400 experts in women and children’s health, nutritional sciences, population health 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’ and Waterloo campuses, our academic programme of teaching, research and clinical practice is embedded across five Departments.

About the role

We are looking for a Research Scientist (Medical) to join our team delivering the EMBRACE study, an innovative research project led by Professor Josip Car. EMBRACE is a visionary, multicomponent international research programme. The first of its kind in the world, supported by Inkfish with £35M core funds over six years. It is a global study of 60,000 participants, including 20,000 mothers, 20,000 infants and up to 20,000 partners.  It brings together world-leading clinician scientists across six distinguished healthcare organisations, exceptional AI and technology companies, together with premier biotech companies, with the overarching aim to fast-track major scientific breakthroughs, revolutionise maternal and early childhood health through precision-personalised interventions, powered by a groundbreaking symbiosis of cutting-edge AI combined with human support.

The successful candidate will partake in the development of personalised, context-sensitive Large Language Models (LLMs) for health coaching. By delivering real-time, adaptive recommendations following exercise sessions and integrating multi-modal health and lifestyle data, the post-holder will play a key role in ensuring the medical validity and real-world relevance of the coaching recommendations. Collaborating closely with AI researchers, the individual will integrate medical knowledge into the LLMs, enhance their social intelligence (empathy, theory-of-mind), and evaluate multilingual and culturally sensitive deployments.

We are looking for an individual with a medical degree, a strong track record in public health and digital health research including peer-reviewed publications, an interest in LLMs as well as experience of using LLMs for healthcare research.

The post is based in the Faculty of Life Sciences and Medicine, and will be closely affiliated with the Informatics Department in the Faculty of Natural, Mathematical and Engineering Sciences.

This is a full time post (35 hours per week), and you will be offered a fixed term contract until 31/07/2029.

About You

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

1. PhD qualified in relevant subject area *
2. Undergraduate medical degree, and Master of Public Health or related field
3. Demonstrable experience in AI/LLMs in healthcare contexts
4. Experience of working in digital health research, from study protocol development to execution and data analysis, with a strong understanding of health data and personalised healthcare
5. Evidence of writing peer-reviewed academic papers
6. Demonstrable experience in medical or healthcare-related research, with evidence of interdisciplinary expertise combining medical and AI domains
7. Strong understanding of natural language processing (NLP) techniques, including tokenisation, embeddings, and transformer architectures and familiarity with statistical methods and evaluation metrics for NLP models, such as F1-score
8. Experience of developing digital therapeutics
9. Excellent project management skills with the ability to initiate, plan, organise, implement and deliver programmes of work to tight deadlines

Desirable criteria

1. Understanding of digital health platforms
2. Ability or potential to contribute to the development of funding proposals in order to generate external funding to support research projects
3. Knowledge of reinforcement learning techniques, particularly Reinforcement Learning with Human Feedback (RLHF), as applied to LLMs
4. Awareness of regulatory frameworks in healthcare research, such as GDPR, HIPAA, or MHRA guidelines as relating to digital therapeutics and health AI

* Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.

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 next page after you click “Apply Now”. This document will provide information of what criteria will be assessed at each stage of the recruitment process.

Further Information

Please note that although this role is advertised as Research Scientist, your contractual job title will be "Research Associate - Inkfish (Medical) in Large Language Models"

We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.

We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's.

We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

We encourage applicants to apply early. We reserve the right to close the advert if suitable applications are received.

To find out how our managers will review your application, please take a look at our ‘How we Recruit’ pages.

This post is subject to Disclosure and Barring Service.