53 phd-structural-engineering "https:" Postdoctoral research jobs at Oak Ridge National Laboratory
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performance models. This position resides in the Materials Engineering Group in the Large-Scale Structures Section, Neutron Scattering Division, Neutron Sciences Directorate at Oak Ridge National Laboratory
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, and measure success. Basic Qualifications: A PhD in materials science and engineering, mechanical engineering, aerospace engineering, polymer science, or a related discipline completed within the last
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, you will collaborate with a dynamic team of scientists and engineers, leveraging cutting-edge resources; most notably the Frontier supercomputer, the world's first exascale computing system. This is a
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the Quantum Heterostructures Group in the Foundational & Quantum Materials Science Section, Materials Science and Technology Division, Physical Sciences Directorate at Oak Ridge National Laboratory (ORNL). As
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visualization technologies, programming systems and environments, and system science and engineering. Major Duties/Responsibilities: The position requires collaboration within a multi-disciplinary research
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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science, mechanical engineering
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, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in
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Requisition Id 15907 Overview: The Radiation Effects and Microstructural Analysis Group (REMAG) within the Materials Science and Technology Division at Oak Ridge National Laboratory (ORNL) is
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challenges facing the nation. We are seeking a Postdoctoral Research Associate who will support the Quantum Sensing and Computing Group in the Computational Science and Engineering Division (CSED), Computing
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and