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Lecturer – PHY1610HS – Scientific Computing for Physicists Course description: Scientific Computing is a graduate course on research computing, covering techniques and methods for reliable and efficient
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, engineering, materials science, maths, or computer science), or equivalent experience Experience with uncertainty quantification or error analysis Familiarity with numerical methods (e.g., Monte Carlo, Finite
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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these nanocomposites, we are looking for a postdoc to further develop high performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core
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independent and collaborative research in theoretical quantum materials. Develop and apply a combination of analytical and state-of-the-art numerical methods to address fundamental problems in condensed matter
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between the Universities of Cambridge and Bonn and numerous international partners, and funded by Stiftung Mercator. The programme investigates how to place the questions of social justice and environmental
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. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages such as python. Experience with HPC environments and linear algebra
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validation experiments for modelling • Computational fluid dynamics techniques • Finite element analysis method • Reviewing literature, planning and managing research, writing technical report / paper
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, numerical optimization, numerical partial differential equations, and parallel computing. The Researcher will join a project developing parallel high-order meshing algorithms from medical images and parallel