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postholder will hold a PhD (or equivalent experience) in a relevant field, with strong knowledge of quantitative and qualitative research methods, including qualitative interviewing, data analysis using
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the study in collaboration with research teams at Nottingham, Oxford, UKHSA and Manchester with expertise in mixed-method policy evaluation, antimicrobial resistance, pharmacy practice research, primary care
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mitigation priorities for the UK. Candidates should also be experienced in conducting quantitative research and applying spatio-temporal epidemiologic methods, ideally to environmental health data. Further
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candidates who are not proficient in Arabic should show commitment to learning. Finally, there is an important fieldwork component of the project, and the successful candidate would be expected to spend
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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project developing Bayesian causal inference methods for mediation analysis using Electronic Health Records (EHR) data. The Research Fellow will design and implement Bayesian methods and software
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, and OpenSAFELY, and to develop and apply the new generation of analytical methods to study environmental health risks and climate change. The post will provide opportunities for interactions with
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research project on cardiovascular risk prediction for people with immune-mediated inflammatory disease. The successful candidate will use advanced risk prediction methods to develop prediction models