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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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in methods of evidence synthesis with a focus on complex public health topics is essential. They should contribute to written outputs, preferably peer-reviewed, consistent with disciplinary
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software packages. They also must have demonstrated knowledge of longitudinal data analysis methods, and an understanding of sample size calculations. Further particulars are included in the job description
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epidemiological or econometric methods, using R software package, and an understanding of techniques used in agent-based modelling. The post is full-time 35 hours per week, 1.0 FTE and fixed term until 31 December
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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