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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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workflows, and distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data
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distributed computing runtime with application and compilers to efficiently integrate quantum processors with HPC resources. The research will explore different quantum computing modalities, their
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developing and managing automated and parallel scientific workflows , including task execution systems, dependency management, and pipeline execution on distributed computing resources. Experience with HPC
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workflows for grid modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The
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systems for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into
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systems for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into
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learning, high-performance computing, scientific modeling, and domain-informed AI to accelerate discovery across DOE mission areas such as energy, materials, biology, nuclear science, autonomous
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motivated and talented postdoctoral research associate(s) to join our dynamic team. This role lies at the exciting intersection of High-Performance Computing (HPC) and Artificial Intelligence (AI) at
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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