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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 successful candidate
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for radioactive s-, p-, and f-block metal ions. Independently execute and troubleshoot complex, multi-step organic syntheses on the multi-gram scale, including reaction optimization, scale-up, purification, and
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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 workflows, and
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. Optimize system and component designs for performance and safety. Develop agentic workflows for scientific computing. Author peer-reviewed papers, technical reports for internal and external release and
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researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance
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, conduct experimental campaigns, perform materials synthesis and analysis, sub-component fabrication, process optimization and integration, and prepare technical documents and research publications. Present
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to additive manufacturing (AM), virtual manufacturing, material characterization, topology optimization, and real-time sensing. This position resides in the Computational Sciences and Engineering Division (CSED
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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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candidate will support research and development projects that advance the state of the art in machining science, machine tool design and characterization, manufacturing process optimization, and digital
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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