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and delay-differential equations. Scientific computing and numerical analysis Large-scale dynamical systems High-performance computing Parallel numerical algorithms GPU computing Strong programming
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Comparative Data & Methods Postdoctoral Researcher Oxford Department of International Development, Queen Elizabeth House, 3 Mansfield Road, Oxford, OX1 3TB The Oxford Department of International
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Science, or a comparable field as well als successfully completed PhD ln-depth expertise in methods of scientific computing and high-performance computing Strong, interdisciplinary teamwork and
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Simon Fraser University | Burnaby (Cascade-Schou / Douglas-Gilpin), British Columbia | Canada | about 2 hours ago
, Canada Simon Fraser University’s Department of Mathematics invites applications for a postdoctoral fellowship in scientific machine learning and numerical methods for weather prediction. The fellow will
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postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel
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, or a related field. Experience with numerical discretization methods for partial differential equations. Experience in computational modeling of geomechanical systems. Knowledge of continuum
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how to use the software. Solid understanding of neuromorphic computing and SNNs, including common neuron models (e.g., leaky integrate-and-fire) and SNN training or conversion methods (e.g., spike
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with expertise in quantum many-body theory, open quantum systems, or quantum information. Experience with advanced numerical methods for quantum systems, stochastic or phase-space techniques, high
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and physical processes relevant to ocean circulation and dynamics. Experience with high-performance computing, parallel numerical models, large scientific datasets, scientific programming, and the
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formal verification with Lean You have experience AI-assisted theorem proving or algorithm discovery is an asset You have working knowledge of numerical analysis, ideally numerical methods for PDEs You