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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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programming in C++ and Python. - Mandatory mastery of GPU programming (CUDA) for optimization. - Experience with Deep Learning frameworks (PyTorch). - Knowledge of the AliceVision architecture is a major asset
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
collaborators # Ability to set individual goals, self-structure and fulfill milestones # Parallel programming, ideally in C++ and with GPUs # Knowledge with Python # Excellent command of English (spoken and
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with excellent facilities for protein science research. There will be direct access to advanced biophysical infrastructure in the biophysics core facility headed by the PI, a GPU cluster with working
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environment with close collaboration between AI experts and leading clinicians Access to unique, large-scale medical datasets and high-performance computing infrastructure (including NVIDIA B300 GPUs) Funding
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communication skills. First-author publications at NeurIPS, ICLR, ICML, AAAI, KDD, or IJCAI. Experience working with large-scale, noisy, or real-world datasets. Experience with GPU-based training and high-performance
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. Skilled in MATLAB and Python. Experience with C++ and GPU programming (CUDA) is an advantage. Ability to work in a team, communicate effectively, coordinate multidisciplinary collaborations, and manage
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development. You’ll have access to state-of-the-art high-performance computing infrastructure and GPU clusters essential for conducting cutting-edge AI, software engineering, and security research. Salary range
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projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities
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significant computational component in deploying multi-GPU codes to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would