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epidemiology, data science, and policy to produce high-quality, policy-relevant evidence with real-world impact. You should have a PhD (or near completion) in public health, epidemiology, data science, applied
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the Oxford–Novartis Collaboration for AI in Medicine. You must hold a PhD/DPhil in Statistics, Statistical Machine Learning, Deep Generative Modelling, or a closely related field, together with relevant
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hold, or be close to completion of, a relevant PhD/DPhil in one of the following subjects: computational genomics, genetic or molecular epidemiology, medical statistics or statistical genetics. You must
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epidemiology, data science, and policy to produce high-quality, policy-relevant evidence with real-world impact. You should have a PhD (or near completion) in public health, epidemiology, data science, applied
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of collaborative projects, working closely with clinicians, imaging experts, and computational scientists across the Oxford–Novartis Collaboration for AI in Medicine. You must hold a PhD/DPhil in Statistics
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difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (eg Operations Research, Management Science, Statistics, Machine Learning, Applied Mathematics
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understanding of dark energy. Projects may span a broad range of topics, including improving Type Ia supernova modelling and standardization, developing and applying advanced data analysis and statistical methods
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gene gain/loss events, horizontal gene transfer, and functional diversification within gene families. You will apply statistical models and machine learning algorithms to identify associations between
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new collaborations within the centre. You must hold a PhD (or near completion) in statistical genetics, functional genomics, computational biology, or a related field together with proficiency in
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37 Faculty of Business, Economics and Statistics Startdate: 01.01.2026 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.12.2029 Reference