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) programme . This is a fixed term position until March 2026. About STARSand HSMA and the work you will be involved with. STARS is an Open Science project that aims to increase the quantity and quality
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of Research Excellence and contribute to a growing cardio-immunology research programme. Inflammation is increasingly recognised as a key modifiable driver of adverse outcomes across a range of cardiovascular
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, calcium imaging, optogenetics and/or behavioural methods. The project is part of a broader research programme designed to use cross-species research to uncover mechanisms for memory in both health and
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bioinformatics/computer science will be essential. Prior experience with connectomics data is highly desirable. Our group has developed an international reputation in this area and our tools have now been used in
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Metabolism (OCDEM) on studies related to circadian rhythms in population health. This post is part of a large, interdisciplinary research programme, offering attractive opportunities to work across
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computer codes to solve some of the daily research problems and have experience with high performance computing. You should have a PhD in Chemistry, Physics or Materials Science with a proven research track
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the Department of Engineering Science at the University of Oxford. The post is funded by the Oxford Martin Programme on Circular Battery Economies. It is fixed term up to December 2027. You will undertake
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neuroscience or a related field. Experience of MEG/OPM-MEG acquisition and data analysis is essential. Experience with MATLAB or Python computer programming for neuroimaging data analysis is essential
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emphasis on research infrastructure and technology rather than preparation for an academic career path. You will be involved in research, but more focused on learning and improving how computing, workflows
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, experience managing and analysing ancestrally diverse genomic datasets, and expertise in computational genetics, including variant annotation and genome-scale analyses. Proficiency in R or Python, excellent