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Field
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mathematical models with state-of-the-art machine learning techniques to develop forecasting models that are more accurate, efficient, and capable of incorporating complex atmospheric phenomena. This is a fixed
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mathematical models with state-of-the-art machine learning techniques to develop forecasting models that are more accurate, efficient, and capable of incorporating complex atmospheric phenomena. This is a fixed
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the discipline, demonstrated by a PhD (or nearing completion) or equivalent in Mathematics and Statistics, to develop teaching programmes, and teach and support learning; Please ensure you read the Job Description
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: Continued development of existing mathematical models of corrosion/ageing. Development of models of reactive transport through polymer foams. Development of a model of competitive solubility when a polymer is
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Research theme: Applied Mathematics, Materials This 3.5 year PhD project is funded by the MADSIM Project (School of Natural Sciences growth fund). Funding is for Home students and EU students with
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PhD studentship in the Groups of “Mathematics Applied to Biology” and “Numerical Analysis and Scientific Computing” at the University of Sussex (UK), with the collaboration of the “Group of
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Type of award Studentship Managing department Department of Mathematics Value This fully-funded studentship includes: Tuition fees at home or international rate Annual maintenance stipend equivalent
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The ProbAI Hub is a £8.5M research hub funded by EPSRC to develop better understanding of and new methods in AI. This will be achieved by research at the interface of AI and Mathematics
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mathematical and experimental study of mode-2 internal solitary waves (ISWs) is proposed. ISWs travel along density interfaces within stably stratified fluids. They are composed of a single or very few pulses
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; University of Sussex | The City of Brighton and Hove, England | United Kingdom | about 2 hours ago
PhD studentship in the Groups “Numerical Analysis and Scientific Computing” and “Mathematics Applied to Biology” at the University of Sussex (UK). PhD project Statistical inference has proved to be