Research Associate / Senior Research Associate in Medical Statistics and Health Data Science

School/Unit: Bristol Medical School Overview Join the University of Bristol to make rigorous causal inference from NHS data faster and more accessible. On this NIHR-funded programme, "Efficient estimation of target trials in electronic health records data", you will develop computationally efficient methods and open-source software for target trial emulation — a powerful approach for estimating the effects of health care interventions from observational data. Current software can take days to run on large datasets; our aim is to make these analyses at least ten times faster and put the tools in the hands of researchers worldwide. You will work with Dr Tom Palmer, Dr Paul Madley‑Dowd, Dr Venexia Walker and Professor Jonathan Sterne in Bristol, and Dr Will Hulme at the University of Oxford, joining Bristol's Electronic Health Records team in the Department of Population Health Sciences — a leading UK centre for data science and causal inference research. Hybrid working is available. Preferable to attend the EHR team meetings in person on a Tuesday and be in the office 2 days per week as a minimum. Responsibilities Develop efficient methodology for sequential target trial emulation, including subsampling strategies, scalable bootstrap inference for large datasets, variable‑length time intervals and vectorised algorithms. Implement the methods in well‑engineered, open‑source R and Python packages (including SEQTaRget and pySEQTarget), with clear interfaces, documentation and diagnostics. Benchmark existing R, Python and SAS implementations, run Monte Carlo simulation studies to evaluate statistical and computational performance. Apply the methods to comparative‑effectiveness analyses using linked NHS electronic health records, including within the OpenSAFELY platform. Work openly and reproducibly using Git and GitHub, collaborate with colleagues across Bristol and the UK, and disseminate work through publications, conferences and software releases. Contribute to research funding bids and, if desired, to teaching and supervision. Qualifications Strong quantitative background in medical statistics, epidemiology, data science or a related discipline; enjoy programming. Experience of statistical software such as R; value open and reproducible research, and want to build high‑quality tools for other researchers. Ability to develop original solutions, work independently while contributing to a team. Experience of Python, R and Python package development, Git/GitHub, SQL, causal inference methods, or NHS data sources (such as Hospital Episode Statistics) is an advantage. Post is suitable for Research Associate or Senior Research Associate level: Senior role requires a relevant PhD (or equivalent) and a track record of peer‑reviewed publications; Research Associate role suits those earlier in their research career with an MSc (or equivalent) and working towards a PhD. Contract Details Contract type: Open‑ended with funding for 3 years from start date (ideally, 01/10/2026–30/09/2029). Work pattern: Full time / 1 FTE. Grade: I/J (Pathway 2). Salary: £39,906 – £44,746 (Grade I) or £43,482 – £50,253 (Grade J). Shift pattern: 35 hours per week. This advert will close at 23:59 UK time on 19/07/2026. #J-18808-Ljbffr University of Bristol - School of Physiology, Pharmacology and Neuroscience