620 parallel-and-distributed-computing-phd-"Multiple" Postdoctoral positions in United Kingdom
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draws together multiple methods and will be guided by a community coproduction to investigate the role of creative engagement with poetry in supporting poetry literacy and wellbeing for young people. A
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research project based in the groups of Prof. Aurora J. Cruz-Cabeza and Prof. John Evans. The project is part of a new £7m EPSRC funded Programme Grant that brings together a team of researchers from
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. Iain McCulloch and is funded by the EPSRC. Find out more about the research group at: About you Applicants must hold a PhD in Chemistry, Physics or a relevant subject area, (or be close to completion
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. There are multiple opportunities to collaborate, take part in network events and undertake research placements within this project. You will have a PhD in Physical Sciences or a closely related field, and you will
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medicine distribution and safety, working at the intersection of research innovation and clinical impact. About The Role Research Associate (Short-Term, 2 Months) – Aseptic Medicine Transport via Drone
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and skills. Good communication skills and travels to other partners across European Union are also required. Main duties will involve in sub-project management and co-ordinating multiple aspects of work
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are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods
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you will manage your own academic research and administrative activities which involves small-scale project management, to coordinate multiple aspects of work to meet deadlines. You will adapt existing
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buildings closed days (and Christmas Eve when it falls on a weekday) for all full time staff. Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles