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to important public health topics. Studies will include descriptive epidemiology and use emulated target trial approaches for robust causal inference within large national health datasets. The post offers
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to achieve a higher degree during the fellowship (e.g. PhD) and will need to have excellent academic and organizational skills, ideally with previous experience of data analysis and/or genetics. About the
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degree, ideally a PhD, in health economics, medical statistics, data science, epidemiology or a related field. A clear conceptual understanding of causal inference methods such as instrumental variable
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Right to work: Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. For further information visit the UK Visas
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, Professors Ruth Keogh and Kate Walker. Applicants should have a postgraduate degree, ideally a PhD, in medical statistics, epidemiology, health economics or a related field. Relevant experience in applying
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PhD) while conducting highly policy relevant research. Applicants should have a postgraduate degree with MRCP or MRCS. Relevant clinical experience in providing cancer treatments, co-ordinating clinical
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analytic codes to investigate the benefits and harms of medications. Candidates must have a doctoral degree (or be within 3 months of anticipated completion of a PhD) in medical statistics or epidemiology
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and contribute to methodological and applied projects in risk prediction using electronic health records data. There is increasing interest in blending concepts of causality into risk prediction models
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research track record and hold a PhD (or equivalent) in a relevant field or have demonstrated the impact of their work through creative practice and dissemination. Candidates are invited to consult
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for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and