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- University of Amsterdam (UvA); Published 21 Nov ’25
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Field
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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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algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational
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the National Recovery and Resilience Plan (PNRR), aims to investigate these mechanisms at multiple levels and with unprecedented precision. In the first phase, GVS will be administered in parallel with
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to analyzing data Knowledge of high-performance computing, such as parallelization, the use of C++, or interfacing with specialized linear algebra packages Other Information: Work arrangement: On-site Candidates