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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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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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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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, collaboration, inclusion and continuous learning. Stakeholder Engagement & Partnerships: Serve as the external interface for the center: liaise with sponsors (DOE, other federal agencies, industry, academia
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(for example, using reinforcement learning) to inform policies to drive atomic manipulation in microscopy experiments leading to discovery and creation of novel states of matter. In addition to fundamental
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Requisition Id 15635 Overview: We are seeking a Research Scientist who will support a growing portfolio of research in cutting edge physics-based machine learning, geophysical modeling, spatial and
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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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treat one another, work together, and measure success. Basic Qualifications: A PhD degree in material science, mechanical engineering, electrical engineering, robotics, or a related field completed within
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Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML models are heavily
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collaborative and open environment? If so, the Oak Ridge National Laboratory’s Learning Systems Group within the Data and Artificial Intelligence Systems section invites you to apply to our new postdoctoral