71 big-data-and-machine-learning-phd Fellowship positions at University of Nottingham in Uk
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to modern slavery in conflict settings. This attempt requires a large, interdisciplinary team working within a cross-cutting framework to connect vast amounts of data and answer many fundamental questions
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the University’s international reputation as a hub for cutting-edge research. Candidates must have a PhD degree in Power Electronics, Machines, and Drives or a closely related field, with a proven track record in
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Bezares (numerical relativity), Dr Stephen Green (gravitational waves, data analysis including machine learning, black holes), Dr Laura Sberna (gravitational waves, black holes, and environmental effects
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About the role - The role-holder will be proficient at quantitative analyses and be support the wider inter-disciplinary research team in analysing quantitative data captured from a variety of
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to modern slavery in conflict settings. This attempt requires a large, interdisciplinary team working within a cross-cutting framework to connect vast amounts of data and answer many fundamental questions
-
to modern slavery in conflict settings. This attempt requires a large, interdisciplinary team working within a cross-cutting framework to connect vast amounts of data and answer many fundamental questions
-
to modern slavery in conflict settings. This attempt requires a large, interdisciplinary team working within a cross-cutting framework to connect vast amounts of data and answer many fundamental questions
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quantitative and digital methods, such as descriptive/inferential statistics, data modelling, machine learning (ML), experimental prototyping and technology ideation. A significant degree of autonomy is required
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.) influences different facets of the experience of ‘successful conversation’ (feeling engaged, exchanging information, etc.). Participants in the studies will include people with hearing loss and hearing-aid
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(particularly under extreme conditions), and/or the use of machine learning for solid mechanics/stress analysis problems are encouraged to apply. The job description presented here is deliberately broad due