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Requisition Id 16261 Overview: We are seeking a Postdoctoral Research Associate who will focus on the physics and materials science of PLD-synthesized twisted oxide and hybrid quantum materials. This position resides in the Neutron & X-Ray Scattering, & Thermophysics group in the Materials...
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and Eligibility Applications will be accepted from January 7, 2026, March 1, 2026, for one position starting as early as May 4, 2026. This position will support one postdoc for two years. You must first
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impactful research and development programs in healthcare informatics, bioinformatics, high performance computing and deep learning. You will work in a collaborative research and development environment
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challenges facing the nation. We are seeking a full-time Senior Artificial Intelligence and Machine Learning Research Scientist who will support the Cyber Resilience and Intelligence Division (CRID) in
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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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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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expected to contribute to the development and application of advanced manufacturing simulations, and machine learning (ML) models relevant to additive manufacturing, virtual manufacturing, material
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connecting molecular dynamics to cellular or tissue-scale processes Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to AI-enabled scientific workflows that couple simulation
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challenging and impactful research and development programs in healthcare informatics, bioinformatics, high performance computing and deep learning. We have a collaborative environment focusing on designing
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Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving