59 postdoc-structural-engineering "https:" "IMT Atlantique" Postdoctoral positions 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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), Energy Science and Technology Directorate (ESTD), at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop physics-based computational models, including Finite Element Analysis (FEA
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Requisition Id 15815 Overview: The Workflows and Ecosystem Services (WES) group under the Advanced Technology Section (ATS) of the National Center for Computational Sciences (NCCS) is seeking a
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challenges facing the nation. The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral
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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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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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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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environment. The successful candidate will develop and apply advanced machine learning techniques—including multimodal AI, computer vision, and large language models—to complex scientific and engineering
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. in Hydrology, Earth system science, Water resources engineering, Computational sciences, Computer sciences or a related field completed within the last 5 years (or expected soon). Demonstrated
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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