186492 – Postdoctoral Research Assistant in Health Data Sciences
Postdoctoral Research Assistant in Health Data Sciences
Location: Botnar Institute for Musculoskeletal Sciences, Windmill Road, Oxford, OX3 7LD
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS)
Grade 7: Salary in the range of £39,424-£43,984 per annum. This is inclusive of a pensionable Oxford University Weighting of £1,730 per year.
This is a full time (100% FTE), fixed term position 2 years.
The University of Oxford is a stimulating work environment, which enjoys an international reputation as a world-class centre of excellence. Our research plays a key role in tackling many global challenges, from reducing our carbon emissions to developing vaccines during a pandemic.
The Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) is part of the Medical Sciences Division and is the largest European academic department in its field, running a globally competitive programme of research and teaching.
The Botnar Institute for Musculoskeletal Sciences enables and encourages research and education into the causes of musculoskeletal disease and their treatment. The Centre provides world-class facilities for scientists in the field of musculoskeletal research.
The NDORMS Health Data Sciences research group is involved in a number of national and international studies exploring the conditions of use (adherence, compliance, off and on-label use) and the safety of licensed drugs and medical devices for the treatment of different conditions in 'real world' (actual practice) settings.
What We Offer
As an employer, we genuinely care about our employees’ wellbeing and this is reflected in the range of benefits that we offer including:
- An excellent contributory pension scheme
- 38 days annual leave
- A comprehensive range of childcare services
- Family leave schemes
- Cycle loan scheme
- Discounted bus travel and Season Ticket travel loans
- Membership to a variety of social and sports clubs
About the Role
We have an exciting and unique opportunity for a postdoctoral research assistant to join our Health Data Sciences group investigating the performance of analytical and design methods to minimize bias in pharmaco-epidemiology, comparative safety and effectiveness, and causal inference research. In this role you will analyse OMOP-mapped real world health data assets following pre-specified protocols and/or using existing analytical pipelines and adapt existing ones to develop new research methodologies for causal inference and real world evidence generation. You will carry out collaborative projects with colleagues in partner institutions, research groups, and key stakeholders as well as collaborate in the preparation of research publications, and book chapters. You will also prepare working theories and analyse qualitative and/or quantitative data from a variety of sources, reviewing and refining theories as appropriate.
About You
You will hold a PhD/DPhil (or near completion) in medical statistics, epidemiology, pharmaco-epidemiology, data science or a related field together with experience in conducting studies using routinely collected health data (RWD). You will have the ability to manage own academic research and associated activities and previous experience of contributing to publications and/or conference presentations. You will have demonstrable expertise in the use of R for statistical analysis as well as the flexibility to learn new skills. Experience programming R packages or related analytical code for OMOP-mapped real world data and of designing, conducting and analysing cohort, case-control, and similar studies using routinely collected health data are desirable.
Application Process
You will be required to upload a covering letter/supporting statement, CV and the details of two referees as part of your online application. Please quote 186492 in all correspondence.
The closing date for applications is 12pm 10 June 2026.
Interviews will take place during week commencing 22 June 2026 and will be hybrid.
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