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will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing
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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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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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laboratory experiments, analyze quantitative data, and effectively communicate scientific results through publications and presentations. Major Duties/Responsibilities: Perform molecular genetics, synthetic
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science and engineering, physics, chemistry, electrical engineering, or a
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-efficient building systems, thermal-fluid processes, industrial heating applications, data center thermal management, and other emerging energy technologies. Develop and operate experimental facilities
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responses to current or imposed environmental conditions. Research may leverage: Laboratory, growth chamber and field experimental data, and/or new measurements to quantify molecular to ecosystem scale
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including tensile, fatigue, creep, impact, and nanoindentation testing. Analyze lattice strain evolution, phase stress, and defect evolution using advanced data analysis tools. Perform alloy fabrication and
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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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strengths in high-performance computing, system architecture, and data analytics with applications in a large variety of science domains. NCCS is home to some of the fastest supercomputers and storage systems