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. This position resides in the Quantum Heterostructures Group in the Foundational & Quantum Materials Science Section, Materials Science and Technology Division, Physical Sciences Directorate at Oak Ridge National
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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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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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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
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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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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Requisition Id 15892 Overview: We are seeking a Postdoctoral Research Associate to conduct advanced materials research focused on the development of cast, additively manufactured, and wrought
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physics, fusion research, life sciences, and materials science. Furthermore, these efforts to enhance data readiness for AI workflows may play a significant role in contributing to the goals of the 2025
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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