20 computer-science-programming-languages-"CNPEM" Fellowship positions at University of London
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to improve people's health in developing countries by striving for excellence in research, healthcare, and training. Our research program spans basic scientific research, clinical studies, epidemiological
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Health Records Research (EHR) Group for an experienced epidemiologist/statistician to join an NIHR-funded programme of research (The INTEGRATE programme) in collaboration with the National Institute
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computational research projects and data infrastructure carried out in the research group. The post-holder will be able to develop research questions within Statistics, Population Data Science, Computational
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health researcher is sought with expertise in quantitative surveys, mathematical modeling in nutrition (especially in Sudan), and crisis clinical nutrition program management. The ideal candidate should
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to the set-up and conduct of a funded research project aiming to co-create a national weight management programme in Thailand. The duties of the post will involve coordinating and writing ethical approval
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to improve people's health in developing countries by striving for excellence in research, healthcare, and training. Our research program spans basic scientific research, clinical studies, epidemiological
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this Fellowship. The Fellowship will be for a period up to 6 months (normally between January and June 2026), based in Paris or London, or split between the two cities. As a Fellow, you will develop a programme of
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, in a relevant topic and relevant experience with mathematical modelling of infectious diseases. Strong knowledge of a programming language (e.g. R, Python) is essential. Experience in mathematical
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experience working with complex datasets, missing information and data quality issues. Programming skills in R, Python or other open-source programming language are required. Further particulars are included
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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