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Field
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artificial intelligence methodologies. The successful candidate will work at the forefront of computational biology, developing novel approaches for large-scale genomic data analysis and contributing
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models, working with Geographic Information Systems, handling large geographical datasets, statistically analysing (geospatial) data, and intensely collaborating with researchers from different disciplines
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artificial intelligence methodologies. The successful candidate will work at the forefront of computational biology, developing novel approaches for large-scale genomic data analysis and contributing
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Research Fellow in Intervention Development to join the Big Data in Health Grou About us Our big data in health team at the University of Southampton is based in the Primary Care Research Centre. We
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, engineering and mathematics. You would be working with world-leading computer science researchers in computer architecture, systems, cybersecurity, machine learning and many other topic, each with interesting
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, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in
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collaboration that has mapped emotional hotspots in four cities in the UK and EU using spatial analysis of social media data. The next phase of the project aims to incorporate explainable AI (XAI) to interpret
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defending the cultural value of knowledge for its own sake. You will also possess computational expertise in data mining and / or analysis, ideally including language processing, and be able to work with an
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experience in data management, analysis, and visualisation. You will demonstrate experience working with large-scale biological datasets, preferably from biobank studies (e.g., UK Biobank, China Kadoorie
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interpretation of atmospheric circulation in high-resolution reanalysis data, idealised model simulations and a state-of-the-art weather forecasting system. The post-holder will have the opportunity to teach