460 parallel-computing-numerical-methods "Simons Foundation" Postdoctoral positions in United Kingdom
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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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. The research will involve both analytical work and numerical computations. The balance between analytical and numerical type work is flexible and can depend on the preferences and skills of the successful
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permeability selection in variant membrane chemistries. This work will run in parallel to experimental analysis conducted at the University of Exeter using synthetic vesicles to observe permeability
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at the interface of Quantum Information and Computation with the study of Complex Quantum Many-Body Systems. For this, we apply a combination of methods from both Mathematics and Physics, complemented by concepts
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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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- Develop original numerical methods for facility simulation in presence of expansion waves - Demonstrate improved estimates of rate constants for two-temperature models - Contribute
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home of several third-party funded research projects. Through cooperation with numerous academic institutions worldwide, the Department is very well connected with the international scholarly community
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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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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