63 cloud-computing-phd-student Postdoctoral positions at Oak Ridge National Laboratory
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potential for high-impact research contributions at the forefront of computational quantum many-body physics. This position resides within the Computational Chemistry and Nanomaterials Sciences group in
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a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in condensed matter physics, material science, chemistry, or nuclear engineer and
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science, computer science, computer engineering, electrical engineering, and optical engineering, and frequently collaborates with partners in industry, academia, and other government organizations
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Director's office can be found here: https://www.ornl.gov/content/research-integrity . Basic Qualifications: A PhD in physics, chemistry, biochemistry or a related field completed within the last five years
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, demand-flexible, and affordable buildings for the DOE Building Technologies Office (BTO), the Federal Energy Management Program (FEMP), and Office of State and Community Energy Program (SCEP). Major Duties
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://www.ornl.gov/content/research-integrity . Basic Qualifications: A PhD in condensed matter physics, material science, solid-state chemistry or a related field completed within the last five years. Preferred
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Requisition Id 15305 Overview: The Computational Hydrology and Atmospheric Science (CHAS) Group at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral Research Associate
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to address scientific and engineering problems, collaborate with leaders in your field and across the laboratory, while working with the world’s fastest computers, and disseminate innovative results through
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safety at ORNL and DOE sites. This position resides in the Performance Engineering group in the Data and AI Systems Section in Computer Science and Mathematics division within Computing and Computational
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science