28 assistant-professor-computer-science-and-data-"Meta" Fellowship positions at University of London
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-led nutrition interventions during the war in Sudan. Responsibilities include developing surveys to assess reliance on community kitchens, analysing pre- and post-war data on food, disease, and coping
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(Maternity Cover) to support teaching on the mentorship programme and the evaluation of the online MSc Sexual and Reproductive Health Policy and Programming (SRHPP) which is co-delivered with the University
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Infectious Disease Epidemiology & Dynamics department at LSHTM to work on polio eradication. This role utilises global surveillance data for polio to inform understanding of the status of eradication and
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Salvador, Brazil. The post-holder will also contribute to the laboratory analysis, data cleaning and management, and data analysis and write-up a study to assess environmental exposures to enteric pathogen
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relevant to your proposed project is desirable. Please note applications without this will not be considered. Please quote reference EPH-DPH-2025-07 or for more information contact Professor Rosie Green
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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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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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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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data from the UK using robust causal inference methods. Based in London at the London School of Hygiene & Tropical Medicine, the post-holder will be embedded within the Electronic Health Records Research
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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