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. Familiarity with diffraction and data analysis tools (e.g., GSAS, GSAS-II, VDRIVE). Experience in alloy fabrication and processing (e.g., arc melting, thermomechanical processing, additive manufacturing
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advanced data analysis tools. Perform alloy fabrication and processing (e.g., arc melting, heat treatment) and relate processing to performance. Apply analytical and modeling approaches to interpret
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-intensive processing of data originating at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable
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data-intensive processing of data originating at scientific edge systems using large-scale HPC/AI computational and storage systems. Coauthor peer-reviewed publications, technical reports, and
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systems. Experience setting up data acquisition systems and implementing signal processing techniques. Experience working with benchtop instrumentation, including power supplies, oscilloscopes, optical
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integrate imaging and other sensing modalities for data collection and monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics
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approaches within the data science, process analytical technology, and chemical processing domains to address technology challenges for raidoistoope production and application. Major Duties
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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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, and processing of materials under high temperature pyrolysis. The candidate will aid staff in conducting high quality research by developing, integrating, and deploying materials to be used for
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. A key aspect involves analyzing and interpreting experimental data to drive product innovation, as well as obtaining and calibrating simulation parameters based on laboratory results. Regular