280 parallel-programming-"Multiple"-"Simons-Foundation"-"EURAXESS"-"Humboldt-Stiftung-Foundation" positions at University of Sheffield in United Kingdom
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Quantum sensing of multiple physical parameters School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Yingkai Ouyang, Dr P Kok Application Deadline: 15 January 2026
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The Japanese Long-Baseline Neutrino Programme (T2K, Super-Kamiokande and Hyper-Kamiokande) School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr S Cartwright, Dr Patrick
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to basis set size. We are working on methods that will allow us to solve the time-dependent Schrödinger equation more quickly. In this project you will develop new (parallel) methods based around accelerated
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and impacts of UK and Northwestern Europe's atmospheric circulation processes. The results of these models will be compared with parallel experiments carried out using Global Climate Models by the other
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technical problems. Collaborate with colleagues from multiple teams to plan workload and resources to fit project timescales. Research into novel technology to advance knowledge, evaluate new techniques, and
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for children and young people with profound and multiple learning disabilities: INTERACT trial www.interacttrial.com The research study is evaluating an interaction and communication approach called Intensive
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complex, involving multiple partners, working on innovative technology to provide practical solutions to industrial problems. As a member of the multi-functional project team, you will contribute
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. (2020) Epithelial–mesenchymal plasticity: emerging parallels between tissue morphogenesis and cancer metastasis. Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 375, No. 1809
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healthcare sustainability and the use of polymeric bio-based materials. Desirable Application/Interview Ability to manage, organise and analyse datasets from multiple sources (e.g., material testing results
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collaboration experience. Main duties and responsibilities Develop findable, accessible, interoperable, and reusable (FAIR) AI / machine learning software, tools, and workflows to support multiple exploratory