Research Fellow in Health Data Science

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 invite applications for a Research Fellow position specializing in applied health data science. This role focuses on using quantitative methods to advance health care research in UK and low- and middle-income countries (LMICs).

The successful candidate will work closely with a research team led by Professor Krishnarajah Nirantharakumar. They will play a key role in advancing health data science methodologies, applying innovative quantitative and AI-driven approaches to address critical public health challenges.

This role offers a supportive research environment with access to specialized training to enhance expertise in health data science and global health research. The post holder will join a department which includes Data Scientists, Statisticians, Health Economists, Qualitative and Quantitative methodologists, Social Scientists, Epidemiologists, Public Health and Global Health experts. The School of Life Course & Population Sciences offers opportunities to build strong networks across departments, research institutions, partner hospitals, and international collaborators.

The role will provide an excellent opportunity to develop your own research profile.

The research will utilise modelling techniques using phenotypic information derived from the electronic clinical records for large populations with long term conditions. The post holder will undertake translational research to address health priorities in UK and low- and middle-income countries (LMICs).

The post holder will join a team that utilises large datasets derived from primary care electronic records, health services and public health research including CPRD.

The post holder will also contribute to capacity building in quantitative research methods in both the UK and LMICs, fostering knowledge exchange and collaboration and contribute to grant applications to secure additional funding for UK-based and global health projects aimed at improving clinical care and health outcomes.

Whilst working within the framework agreed with the post holder's line manager/s, the post holder is expected to manage their own workload and is expected to be flexible in anticipating and responding to shifting and competing demands on their and the wider team's time. It is expected that the post will work with minimum regular supervision but will refer to their line-managers for advice on issues which fall outside existing regulatory or policy frameworks.

We welcome applications from motivated researchers eager to make a meaningful impact on global health through cutting-edge data science.

This is a full time post (35 hours per week) offered as a fixed term contract for 2 years.

About You

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

Essential criteria

1. PhD in health data science, medical statistics or machine learning methods
2. Advanced knowledge of electronic healthcare records and their use in development and validation of risk prediction models
3. Knowledge in application of econometrics in research
4. Advanced knowledge of how to build research capacity in LMIC settings
5. Evidence of application of statistical analysis and machine learning methods in R
6. Knowledge of current trends, research methods, and best practices in risk prediction and multistate models' development and validation especially in multimorbidity research
7. Knowledge on causal inference methods such as application of DAGs in confounder adjustment
8. Committed to equality, diversity, and inclusion, actively addressing areas of potential bias

Desirable criteria

1. Experience of working with multiple nations on global health
2. Evidence of building research capacity in LMICs settings

Further Information

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.

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

We are able to offer sponsorship for candidates who do not currently possess the right to work in the UK.#J-18808-Ljbffr