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frameworks, which may include OpenFOAM, CaNS and nekRS, on CPU- and GPU-based systems. Applicants should already have experience with comparable simulation tools and High Performance Computing (HPC
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fermionic many-particle quantum systems. modeling work in collaboration with experiment. publications on research results obtained in the project. transfer of knowledge of HPC and code work in the
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genomics, proteomics, or metabolomics. Experience with relevant machine-learning frameworks, such as PyTorch, and high-performance computing (HPC) environments. Knowledge of molecular and cellular biology
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training and resources in computational science, including high-performance computing (HPC) clusters. For more information about the DDLS program, see here. The employment In connection with your admission
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computing (HPC) environments Turbulence modelling and numerical methods Control theory and flow control Linux, version control (e.g., Git), and LaTeX Excellent academic results are also considered a
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extensive training and resources in computational science, including high-performance computing (HPC) clusters. For more information about the DDLS program, see here. The employment In connection with
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integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine-learning frameworks, such as PyTorch, and high-performance computing (HPC) environments