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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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on representing the field of parallel programming in the Bachelor and Master study programs of TU Wien, as well as contributing to usual management and faculty service tasks. Where to apply Website https
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Proven experience with multiple parallel programming paradigms, including but not limited to; MPI, OpenMP, and CUDA (Compute Unified Device Architecture) Experience in a batch HPC environment with a
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/C++, Fortran, and/or Python Proven experience with multiple parallel programming paradigms, including but not limited to; MPI, OpenMP, and CUDA (Compute Unified Device Architecture) Experience in a
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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invested in new laboratories and maker space at the centre of the Strand campus in the heart of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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ecomorphological outcomes significantly parallel. As a next step, it is vital to dig more deeply into the molecular mechanisms driving these patterns. This project will examine replicate divergences into specialist
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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 solver, capable of running