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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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supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized
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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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processors (CPUs/GPUs), custom accelerators, and next-generation quantum and analog devices . Your work will contribute to a vision of autonomous system engineering in which AI agents understand
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processors (CPUs/GPUs), custom accelerators, and next-generation quantum and analog devices. Your work will contribute to a vision of autonomous system engineering in which AI agents understand
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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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opportunity to work with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The
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and conferences. To conduct this work, the successful candidate will use the world's first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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, ASE, GROMACS, OpenMM, TorchSim , or related MD simulation packages. Experience with MLIP-based molecular dynamics , including system preparation, simulation execution, trajectory analysis, and
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stack, lifecycle-managed nodes, TF2, rosbag workflows, and distributed system diagnostics. Experience with CUDA and deployment on NVIDIA edge-GPU platforms (Jetson Orin, Thor, or Spark). Experience