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of mathematical proof automation: You identify and address research questions in the field of mathematical automation in numerical analysis or approximation theory. You implement the developed methods in Lean. You
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing
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mathematical and computational techniques, it is essential to have experience in mathematical modelling / dynamical systems theory / numerical methods / coding. An ideal candidate would have a PhD, or
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out research work, analytically and numerically, jointly with the co-investigator and the project research team in the area of inference, information build-up and learning methods in the general context
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than 30 people, including parallel post-doctoral researchers in four other countries, and led by Prof Ben Baumberg Geiger at King’s. Secondly, it’s located within the Centre for Society and Mental Health
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a catch: in most real-world scenarios, this equation lives in very high-dimensional spaces, making its numerical solution a serious computational challenge. This project aims to tackle that head-on by
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than 30 people, including parallel post-doctoral researchers in four other countries, and led by Prof Ben Baumberg Geiger at King’s. Secondly, it’s located within the Centre for Society and Mental Health
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a testbed of micromorphic numerical models, and metamaterials. Proposing experimental methods to obtain micromorphic models under small and large strain, with coupled uncertainty quantification
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a testbed of micromorphic numerical models, and metamaterials. Proposing experimental methods to obtain micromorphic models under small and large strain, with coupled uncertainty quantification
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will