"Senior Lecturer/Reader in Applied Artificial Intelligence for Better Health & Care (Education & Research)"

Grade and Salary:

Senior Lecturer £65,091 - £74,613;
Reader R1-R5: £66,884 - £74,613 per annum, including London Weighting Allowance

About the Role

The Better Health & Care Hub (BH&CH) is a cross‑faculty initiative led by the Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care (NMPC), working with the Faculty of Social Science & Public Policy (SSPP) and the Institute of Psychiatry, Psychology & Neuroscience (IoPPN). The Hub partners with NHS, social care, community, patient‑led, charitable and international organisations to drive transformation in care for people with complex health and social needs. Its focus is on redesigning interventions, pathways and systems through research, education and innovation.

We are seeking an exceptional Senior Lecturer or Reader in Applied Artificial Intelligence (AI) to join BH&CH and the Digital Health & Applied Technology Assessment Division within NMPC. This is an exciting opportunity to lead ambitious interdisciplinary research at the interface of AI, digital health and applied clinical science, and to shape new postgraduate programmes in Digital Health and AI.

You will develop AI‑driven solutions for people with multiple long‑term conditions, comorbidities, complex care needs, advanced illness or palliative care requirements. You will contribute to BH&CH strategic themes (Careforce, Frugal Innovation, Communities) while fostering national and international collaborations.

This role includes:

• Leading high‑impact, externally funded applied AI research programmes.
• Designing and delivering postgraduate education in Digital Health and AI, and contributing to wider MSc, undergraduate and doctoral teaching.
• Supervising PhD students and contributing to capacity‑building across interdisciplinary teams.
• Developing partnerships across health, social care, academia, and industry to advance equitable, scalable and transformative AI approaches.

This is a full-time (35 hours), indefinite post, with hybrid working (minimum two days per week on the Waterloo campus).

About You

Further information and more detailed criteria can be found on our website.

Senior Lecturer – Essential Criteria

• PhD or medical equivalent degree.
• Sustained and upward trajectory of peer‑reviewed publications, demonstrating originality, rigour and significance in applied AI aligned to BH&CH and Faculty themes, with growing international recognition.
• Evidence of securing external research funding, including a clear future funding plan and potential to lead a research group delivering innovative AI programmes.
• Demonstrated development of research methods in applied AI, contributing to interdisciplinary health and care research.
• Experience in PhD supervision (primary, secondary or panel roles).
• Strong knowledge of AI evidence‑based practice; ability to supervise MSc/undergraduate projects and critically appraise research.
• Excellent teaching experience at MSc/UG level, including curriculum, assessment and module development.
• Proven ability to translate applied AI research into compelling teaching for interdisciplinary learners.

Reader – Essential Criteria

• PhD or medical equivalent degree.
• Internationally recognised track record of world‑leading or internationally excellent publications in applied AI, demonstrating research leadership and innovation.
• Strong and sustained success in securing external funding and leading research groups delivering innovative AI programmes.
• Internationally recognised methodological expertise in one or more areas relevant to applied AI in health and care (e.g., trials, mixed methods, big data, statistics, co‑design, implementation science).
• Evidence of leadership in high‑quality education, curriculum development, assessment initiatives and successful PhD supervision with timely completions.
• Up‑to‑date knowledge of AI evidence‑based practice; ability to supervise MSc/UG projects and critically appraise advanced research.
• Strong teaching experience in applied AI topics, including module design and translation of AI research into practical learning for interdisciplinary audiences.