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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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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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awareness and understanding of open relevant research conducted throughout the DOE complex and the world. Basic Requirements: PhD in Mechanical Engineering, Electrical or Computer Engineering, or related
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Distinguished Staff Scientist (Cancer Biology and Radiopharmaceutical Therapy Development and Advanc
, proliferation, biomarker expression, and off-target effects. Integration of cancer biology with radiochemistry, isotope science, dosimetry, imaging, data analysis, and clinical collaborations to support
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computer-controlled instrumentation for online or at-line measurements. Develop quantitative calibration, signal processing, chemometrics, uncertainty evaluation, data acquisition, and analysis workflows
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) advanced cooling solutions for energy systems; (3) thermal management for electronics and data centers; and (4) separation processes for building environments. Develop experimental setup, problem-solving
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testing of mechanical systems, data analysis, and interpretation of test results including modal analysis. Experience with mechanical system measurements, including instrumentation, signal processing, and
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data analytics, and manufacturing process optimization. Develop and apply models, algorithms, or data analysis workflows to support machining process understanding, machine tool characterization, process
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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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, instrumentation, and data acquisition systems; conduct laboratory and field testing; and perform thermodynamic analysis, system modeling, and performance assessments. Analyze and interpret experimental and modeling