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Field
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research practices Experience training and deploying machine-learning models on GPU-based systems; familiarity with HPC environments is an advantage Interest in interdisciplinary research at the interface
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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and writing scientific code - Knowledge of at least one of the parallel programming paradigms (MPI, OpenMP, GPU) - Proficiency in both spoken and written English is essential (work will be carried out
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 3 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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. 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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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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will focus on the development of GPU-accelerated GPAW software based on density functional theory (DFT) for constant-potential calculations within a plane-wave framework. The developed software will be
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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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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