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analyzing and interpreting experimental data to drive product innovation, as well as obtaining and calibrating simulation parameters based on laboratory results. Regular interfacing between experimental and
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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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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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biomedicine and health. It provides foundations and advances in quantum information sciences to enable quantum computers, devices, and networked systems. It develops community applications, data assets, and
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Requisition Id 16779 Overview: We are accepting applications for Postdoctoral Research Associate positions in Data Science for Advanced Manufacturing that will focus on the development of next
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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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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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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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-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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. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding. Names and contact information for three