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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 candidate is expected
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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
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, digital manufacturing capabilities, and data-driven approaches to accelerate the design, optimization, and scale-up of high-performance composite manufacturing processes. This position resides in
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tool metrology, process monitoring, manufacturing process optimization, and related manufacturing technologies. Serve as a senior technical contributor and project lead for medium to large research
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, electrical engineering, or a related discipline obtained in
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computing solutions to efficiently scale testing environments supporting large datasets and high-performance AI workloads. Optimize resource allocation for simultaneous testing tasks and real-time tracking
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computing solutions to efficiently scale testing environments supporting large datasets and high-performance AI workloads. Optimize resource allocation for simultaneous testing tasks and real-time tracking
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., YAML/JSON), and implement robust logging, error handling, and checkpoint/retry strategies. Operational Reliability and Optimization: Diagnose job failures, mitigate bottlenecks, and improve throughput
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature
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ideas and writing proposals that establish new projects Understanding for creating, modifying, and analyzing OpenStudio and EnergyPlus building energy models Design, implement, and optimize scalable