10 advance-structure-modeling Postdoctoral positions at Oak Ridge National Laboratory
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, and physics based computational modeling of joining processes or performance of welded structures. As a postdoc, you will conduct research and development at the forefront and often at the intersection
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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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automated fiber placement Apply advanced constitutive material models for polymer composite behavior under processing conditions Collaborate with multidisciplinary research teams on simulation, manufacturing
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on using neutron scattering to probe the structure and dynamics of materials relevant to energy applications and/or structural materials. In particular, the work will aim to (1) understand how hybridized
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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at ORNL, along with computational tools for integrated atomistic modeling in support of materials research for extreme environments. The candidates will develop and apply advanced experimental
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carlo), as well as experience in developing and/or applying advanced AI/ML methods to accelerate materials discovery. The project will involve integrating such theory-informed AI-models for creating
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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
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Requisition Id 15684 Overview: We are seeking a Postdoctoral Research Associate to help advance our understanding of quantum magnetic materials through materials synthesis and characterization
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and