11 structures-"https:"-"https:"-"https:" research jobs at Oak Ridge National Laboratory
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Requisition Id 15794 Overview: The Physics Division at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to join the Nuclear Structure and Nuclear Astrophysics
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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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. Research will involve growth of single crystals and measurements to understand their structural and physical properties including magnetism and thermal transport, as well as helping to identify new magnetic
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characterizations. Experience with user facilities. Data analysis of structural, electronic, magnetic, and topological properties. Work with others to maintain a high level of scientific productivity. Publish
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
-state physics, ferroelectrics and/or 2D materials. Strong background in developing and/or applying materials simulation methods, such as atomistic simulations using electronic-structure and/or machine
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Requisition Id 15537 Overview: We are seeking a Postdoctoral Research associate in computational nuclear physics. This position focuses on nuclear theory with an emphasis on nuclear structure and
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frequency domain reflectometry (OFDR), and other technologies relevant to nuclear reactor structural health monitoring and maintenance. The candidate will be expected to install, configure, analyze, and
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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
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and nuclear structure and reactions. The position is part of the nuclear theory team that resides in the Theoretical and Computational Physics group in the Physics Division, Physical Sciences
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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