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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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on emerging architectures and performance for advanced computing. This position resides in the Architectures and Performance Group (APG) in the Computer Science and Mathematics Division at Oak Ridge National
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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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or CP2K on massively parallel architectures. Solid skills in common scientific programming languages, in particular languages such as FORTRAN, C, C++, and Python Experience in parallel programming with one
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understanding of thermal-fluid system architecture, including component-level behavior (e.g., heat exchangers, compressors, refrigerant cycles, control valves) and their integration into system-level performance
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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environment. Preferred Qualifications: Experience with AI/ML approaches for spatiotemporal or Earth system data, including transformers, multimodal learning, representation learning, or related architectures
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
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include the porting of lattice QCD libraries to the Exascale Architectures (e.g. Frontier at OLCF) and/or emerging programming models (HIP, SYCL, Kokkos, etc), software optimization and/or algorithmic