University of Sheffield
The School of Health and Related Research (ScHARR) is looking to recruit an early (G7 Research Associate) or mid-career (G8 Research Fellow) researcher with strong background in statistics and/or econometrics, preferably in the area of causal inference, with an interest in evaluating the efficacy and comparative effectiveness of care technologies/services, to join the Health Economic and Decision Science (HEDS) section.
You will mainly work in conjunction with the ScHARR Knowledge Exchange (KE) team, providing support in the development of non-randomised study designs and associated analyses for the evaluation of care technologies or services. You will also provide statistical and/or econometric support to multi-disciplinary teams as part of other HEDS research and consultancy projects. Work will include the evaluation of new care technologies or services, usually within non-randomised study designs with a focus on the efficacy and/or comparative effectiveness of the care technology. These evaluations could be supported through the use of primary data collection or reliant on existing ‘real-world data’ (RWD) sources.
Applicants should be familiar with methods for estimating comparative effectiveness using non-randomised data, such as
those methods detailed within NICE Technical Support Document 17:
https://www.sheffield.ac.uk/sites/default/files/2022-02/TSD17-DSU-Observational-data-FINAL.pdf You will be encouraged to develop your own methodological research interests that are complementary to those within HEDS and help contribute to Masters level teaching and supervision.
You should have a postgraduate degree in statistics and/or econometrics, or other relevant discipline (or equivalent experience). A PhD (or equivalent experience) in statistics, or a relevant quantitative discipline, is essential for a Research Fellow.
Closing date for applications is Sunday 22nd January 2022 at 23:59 (UK time).
For informal enquiries about this job and department, contact: Matthew Franklin on email@example.com
To apply for this job please visit jobs.shef.ac.uk.