Research Fellow in Health Data Science

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

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

Desirable criteria

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

Advert information