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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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areas include: Parallel and distributed graph and or ML algorithms — shortest paths, connectivity, clustering, or graph traversal on GPU clusters and distributed-memory systems. Sparse direct and
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exascale and post-exascale systems. Drive research into next-generation storage systems and hierarchies, including storage-class memory, disaggregated storage architectures, high-bandwidth parallel file
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: Develop and evaluate architectural models, system-level simulators, and performance modeling frameworks for QHPC systems, capturing relevant characteristics of quantum processing units, classical HPC
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on high-performance computing (HPC) environments using distributed data or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi
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discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments
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methods through which AI agents guide architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance
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Experience in multiphysics modeling Experience in parallel programming on large-scale computational clusters Expertise in Matlab programming Active TS/SCI Clearance is preferred Special Requirements
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methods through which AI agents guide architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance
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parallel filesystems Special Requirements: This position requires the ability to obtain and maintain a clearance from the Department of Energy. As such, this position is a Workplace Substance Abuse