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Field
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managing and administering an NVIDIA DGX SuperPod instrument. You and another HPC administrator will partner closely with a team of data scientists from Stanford Data Science to ensure that the GPU cluster
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-procedure-unit-gpu-30hrs Return to Search Results
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management application GPU & specialized hardware for Artificial Intelligence and Deep Learning Server-class hardware and remote management The HPC administrators are responsible for working closely with
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-performance computing, including parallel or GPU programming (MPI, OpenMP, CUDA, Kokkos, etc.) Familiarity with modern software development practices, including debugging, profiling, and version control
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neural networks to harden object detection and semantic segmentation, as well as maintaining the real-time performance of the hardened AI computing on embedded GPUs (NVIDIA Jetson AGX Orin). RGB camera and
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in chemistry, physics, or related field. At least 2 years of experience developing quantum Monte Carlo algorithms. Strong problem-solving and analytical skills. Python programming experience. GPU
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and train CNN and SNN models utilizing frameworks such as Keras, PyTorch, and SNNtorch Implement GPU acceleration through CUDA to enable efficient neural network training Apply hardware-aware design
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& GPUs — and deep integration with clinical data including electronic health records A diverse array of ongoing research, education, patient care, and community centered activities that require increasing
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of research computing at LSE. Your expertise will be key in future-proofing our research hardware environment, ensuring high availability, scalability and security across HPC clusters; GPU acceleration, high
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. Prior work with databases used for organizing large-scale processing. Prior use of and/or software development with GPUs. Experience with visualization of large data sets. An understanding of how to make