14 structural-"https:"-"https:"-"Babes-Bolyai-University" Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 16127 Overview: We are seeking a Postdoctoral Research Associate who will develop and apply electronic structure theory approaches to examine the fundamental properties of a range
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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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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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, 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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-based systems Background in topology optimization and structural design Experience in thermomechanical characterization of polymer materials Demonstrated experimental capabilities and a strong
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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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structure and statistical mechanics codes, and data science tools would be highly desirable Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and
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of NTI and CNMS to develop HPC workflows that can perform multi-fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques Develop physics-informed
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properties of the above materials. Collaborate with ORNL postdocs and staff who are involved in structural characterization. Participate in the development of new ideas and projects. Present and report
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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