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About us Our Big Data in Health team at the University of Southampton is based in the Primary Care Research Centre. We are an interdisciplinary group conducting innovative research to address
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data Apply knowledge in a way which develops new intellectual understanding Disseminate research findings for publication, research seminars etc Supervise students on research related work and provide
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different ML architectures for postprocessing precipitation forecasts over India. Determine how to maximise information extracted from the raw forecasts and how to optimise postprocessing skill for heavy
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following skills and experience: Essential criteria PhD qualified in relevant subject area Experience working with large datasets e.g. CPRD or similar Experience with relevant statistical software (STATA or R
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for Research Staff Development for more information. About You To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in
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to contribute to a world-leading project on Survey Data Collection Methodology Survey Futures https://surveyfutures.net/ . Survey Futures is a collaboration between nine universities and three survey
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tenable for up to 4-years. Applications are welcome from graduates of any university. Candidates will usually have completed their PhD, but must not have undertaken more than 3-years of postdoctoral work by
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the direction of Dr Maslovskaya, you will be part of a large multi-institution team of survey data collection methods experts in the UK. You will analyse probability-based NatCen online panel data collected using
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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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, development, implementation, and coordination of research and laboratory protocols to investigate transmission of enteric pathogens in low-income households of Salvador, Brazil within the context of large-scale