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clinicians; (b) support medical clinicians who have developed and continue to sustain an academic career in parallel with their clinical responsibilities; (c) support the ongoing development
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 10 days ago
operates the first Czech quantum computer named VLQ. https://www.it4i.cz/ Activity description: · development of a Python application with a graphical interface, · compilation of parallel applications and
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of advanced functional imaging methodologies for whole-brain mapping of neuronal activity (such as calcium functional imaging, nonlinear microscopy, and light-sheet microscopy), in parallel with monitoring
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National Lab, University of Tokyo etc.), the PhD candidate is expected to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient
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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
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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
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to run on parallel environments. CUDA and Fortran programming expertise. Experience with HPC job schedulers, especially job submission is required. Slurm scheduler experience. Experience and understanding
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 10 days ago
operates the first Czech quantum computer named VLQ. https://www.it4i.cz/ Activity description: development of a Python application with a graphical interface, compilation of parallel applications and
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in parallel with timelines, milestone markers, and outcomes. Support quarterly reporting requirements Required Qualifications* Must be a current UM-Flint staff Bachelor's degree (3) or more years of
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and