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facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and
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for someone passionate about advancing the integration of inverter-based resources into the power grid. The selected candidate will be involved in developing power electronics and hardware in the loop
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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for deployment on large-scale computing resources, such as high-performance computers (e.g., Perlmutter, Aurora, etc.). This includes tasks such as automating model design, optimizing hyperparameters, and training
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for projects related to the integration of microgrids, distributed energy resources (DERs), and advanced transmission & distribution (T&D) coordination. This role is ideal for a researcher passionate about
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-of-the-art imaging facilities at the APS and top-20 supercomputing resources at Argonne National Laboratory. Position Requirements This level of knowledge is typically achieved through a formal education in a
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, and multimodal information. This position offers the opportunity to work with some of the world’s most advanced computing resources, including Exascale supercomputers, and to collaborate with leading
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, computational physics and x-ray science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of the world’s largest supercomputers (Polaris
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(H2, NH3). The successful candidate will leverage high-performance computing (HPC) resources at the Laboratory to perform CFD simulations of low-carbon fuel injection, mixing, combustion, and emissions
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researcher will conduct advanced research to develop sustainable, innovative bioprocesses and separation technologies to improve the efficiency of resource and energy utilization; to minimize our dependence