This is an exciting opportunity to join an energetic and thriving research group, internationally recognised for excellence in applied public health research, with a shared concern for health equity being a defining ethos.  You will be part of the Health Inequalities Policy Research (HIPR) Group, in the Department of Public Health, Policy and Systems at the University of Liverpool. The HIPR vision is to improve health and reduce inequalities through the study of the determinants of health and wellbeing and the policies that impact them.

 

You will work within our new Civic Health Innovation Labs (CHIL). CHIL brings together academia, the NHS, local government, charities, and industry to develop new models for progressive data use and responsible artificial intelligence You will work with linked health and social care data across 2.6 million population through the Combined Intelligence for Public Health Action (CIPHA) platform, delivering applied research for public health action.

 

We are looking for an enthusiastic population health data scientist / epidemiologist / statistician / data and signal processing engineer committed to utilising data to improve population health, find out what works to build a healthy, resilient community, and address health inequalities. You will contribute to a number of research programmes, including the Mental Health Research for Innovation Centre (M-RIC), the NIHR Oxford Brain Health Biomedical Research Centre ¿ Mental Health in Development Theme, and the Networked Data Lab.

 

You will have a PhD in a highly numerate relevant area (e.g. epidemiology and/or public health, health data science, health economics, psychology, health service research, computer science, signal processing, mathematics and/or statistics), with an excellent working knowledge of research methods in epidemiology and statistics, including experience in three or more of the following: managing large datasets; data linkage; quantitative analysis of large observational datasets; longitudinal or panel data analysis methods; risk prediction models/ machine learning/ causal inference methods/ signal and data processing and optimisation. You will be enthusiastic and committed to working in a field of health inequalities research, have excellent communication and project management skills, with the ability to engage sensitively with members of the public and to work effectively with a wide range of partners, including academics, local authorities, NHS organisations, children, young people and their families, and community groups.

 

 

 

Although the university has a hybrid working system, the expectation is that the successful applicant appointed to this role will be expected to work more than 50% on campus.

 

The University has the right to close the vacancy early if it is deemed that there have been enough applications received.