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Associate with mathematical modelling and numerical/data analysis background to join our food system resilience project, led by University of Reading, joining a large interdisciplinary team with an excellent
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, with expertise both in theoretical methods and in numerical study, and with a particular focus on the application of quantum information driven tools, such as tensor networks or convex relaxations
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atmospheric physics, meteorology, climate, numerical methods, and data science. The Research Associate will be proficient in programming/scripting (e.g., in Python, and/or R, and/or Matlab, and/or Bash script
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Pregnancy. Current risk-communication materials in maternity care are often text-heavy, numerically dense, and disconnected from the lived experiences of women and families. Building on prior epidemiological
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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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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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plasma channels, and methods for controlling injection of electrons into laser-driven plasma wakefields. This work will be undertaken within the research groups led by Prof. Simon Hooker (Oxford), in
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the following areas: numerical analysis of PDEs (with deterministic and/or stochastic methods), Gaussian Random Fields, mathematical foundations of deep learning, functional analysis and measure theory
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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