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• Familiarity with operating HPC clusters (e.g., bash, Python) Preferred Qualifications • HPC programming skills (e.g., modern Fortran or C/C++) • Parallel programming skills (e.g., OpenMP, MPI, OPENACC, CUDA
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closure modeling and/or high performance computing environments (MPI, CUDA) • Expertise in software development and computing tools (C/C++, python, git, parallel computing, etc.) • Experience with deep
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lattice field theory and numerical methods, with experience in HPC programming (e.g., C++, Python, MPI, OpenMP, CUDA) and parallel computing environments. - Experience in performance analysis, debugging
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computer science or related computational engineering disciplines. Experience with simulation frameworks for complex computer systems and architectures. Some knowledge of accelerator (CUDA, SYCL, HIP) and scientific
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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candidates with experience in software design, Python and CUDA coding, molecular dynamics calculations, and model building. Knowledge in physical chemistry, Bayesian statistics, machine learning, computational
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between models and experiments), Developing empirical force fields Developing scientific software and workflows Experience in programming for HPC environments, including MPI, OpenMP, or CUDA
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Max Planck Institute for Solid State Research, Stuttgart | Stuttgart, Baden W rttemberg | Germany | 3 months ago
prioritize candidates with experience in software design, Python and CUDA coding, molecular dynamics calculations, and model building. Knowledge in physical chemistry, Bayesian statistics, machine learning
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order to expedite the simulation, MagTense is based on a core implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs
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order to expedite the simulation, MagTense is based on a core implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs