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compensation package with comprehensive health and welfare benefits. A supportive team environment that promotes collaboration and knowledge sharing. Access to world-class computational infrastructure, GPU-based
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supportive team environment that promotes collaboration and knowledge sharing. Access to world-class computational infrastructure, GPU-based computing environments, and unique high-quality cryoET datasets
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empowers researchers to solve complex problems through a massive ecosystem of 50,000+ CPU cores, 1,000+ GPU resources, and 50PB+ of storage. At CHPC, we foster a positive, collaborative environment where
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/GPU environments. Provide consultative support and training to researchers using BRC AI/ML tools and pipelines. Performs related duties & responsibilities as assigned/requested. Qualifications REQUIRED
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(URCF) at Drexel University is building a new shared computing platform focused on GPU-accelerated workloads, particularly AI model training. The system includes GPU and CPU compute nodes with Nvidia H200
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heterogeneous (CPU/GPU) computing models. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and
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Job Code 0005 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The successful applicant will assist in the adaptation of the PPMstar code to run well on GPU-accelerated
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experiments, particularly ATLAS and DUNE. Contribute to the architecture and core development of the Phlex framework, emphasizing scalable, multi-threaded, and heterogeneous (CPU/GPU) computing models
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heterogeneous (CPU/GPU) computing models. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and
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and collaborative team of computational scientists, software and AI engineers, and neuroscientists, you’ll have access to high-performance workstations, CPU/GPU clusters, and experimental systems