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broadly with faculty in the Luddy School of Informatics, Computing, and Engineering, Data Science, Statistics, the Institute for Scientific Computing and Applied Mathematics, and scientific units across our
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geometries, and optimization of surface and volumetric meshes. The fellow will also develop and evaluate multiphysics computational models coupling microwave electromagnetic energy deposition with bioheat
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Tech, including PhD programs in Bioinformatics; Quantitative Biosciences (QBioS); Algorithms, Combinatorics, and Optimization (ACO); Computational Science and Engineering (CSE); and Machine Learning (ML
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, or Mathematics Strong programming skills in Python and R Background in algebraic geometry, computational algebra, and symbolic computation Experience with algorithm development and research Preferred
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broadly with faculty in the Luddy School of Informatics, Computing, and Engineering, Data Science, Statistics, the Institute for Scientific Computing and Applied Mathematics, and scientific units across our
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primary appointment in Mathematics and opportunities to collaborate broadly with faculty in the Luddy School of Informatics, Computing, and Engineering, Data Science, Statistics, the Institute
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discretization techniques, high performance computing, mesh generation, and geometry representation for a wide variety of physics applications. Our intention is to integrate computational modeling with
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emphasis on emerging wearable optically pumped magnetometer (OPM) technology. These methods and hardware will improve the spatial resolution, robustness, and computational foundations of MEG source imaging
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prior experience in large-scale calculations using CFD Strong skills in CFD for complex geometries, and parallel computing Development of advanced algorithms for problems involving very large mesh (tens
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discretization techniques, high performance computing, mesh generation, and geometry representation for a wide variety of physics applications. Our intention is to integrate computational modeling with