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Field
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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seeks candidates with interests in Statistical research at the interface of machine learning and AI. They will have the skills and enthusiasm to lecture graduate level, over a wide range of topics within
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, cluster randomised controlled trials implementation science, data linkage, data science, machine learning and artificial intelligence. In this role, you will have the opportunity to engage in a series of
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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experience contribute to ongoing translational research program related to the application of statistical and machine learning methods in reproductive and perinatal medicine using both clinical quality
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time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
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strong analytical skills and desirably some computer modelling experience, and an ability to work in a multidisciplinary team and engage confidently with partners. You will have a track record of
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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the very people who bring those lessons to our students. Every bit of knowledge, and every advancement, provides our students with an unprecedented, collaborative learning environment, and helps improve