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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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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
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Drakeley and Dr Julius Hafalla, primarily focused on immunoepidemiology, as needed. The post holder will be responsible for the generation and curation of laboratory and epidemiological data from different
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science methods, ideally to environmental health data, and conducting systematic reviews and meta-analysis. Further particulars are included in the job description. The post is full-time 35 hours per week
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experience in lab-based microbiology, virology and cell culture techniques. Further information is available in the job description/person specification. The post is full-time 1 FTE and fixed-term until 31st
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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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. The post-holder must have a postgraduate degree, ideally a doctoral degree (for Research Fellow) or a doctoral degree (for Assistant Professor) in epidemiology, medical statistics or health data science
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paragraphs addressing each criterion. The supporting statement is an essential part of the selection process and thus a failure to provide this information will mean that the application will not be considered
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statement is an essential part of the selection process and thus a failure to provide this information will mean that the application will not be considered. An answer to any of the criteria such as "Please
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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