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modeling, multiscale approaches) to support materials development and manufacturing process understanding. Use AI, machine learning, and data-driven methods as enabling tools to accelerate experimentation
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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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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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Qualifications: Advanced degree (MS or PhD) in Computer Science, Data Science, Geospatial Science (GIS/remote sensing), Electrical/Computer Engineering, or a closely related discipline. Minimum of 10–12 years
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, JAX etc.) Two or more years of experience in applying machine learning methods for instrument control, such as on a microscope, or on a nanomaterials synthesis platform resulting in publishable
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research, operations, and community engagement, and work cooperatively to leverage scientific capabilities across ORNL. Work in a highly collaborative environment with data scientists, machine learning
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analytics that enhance and evolve business operations and scientific decision-making capability and related activities at ORNL.Qualified applicants will have a solid foundation of Generative AI and Machine
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injection techniques, space charge simulation and theory, and control and mitigation of beam halo and other beam loss mechanisms, and machine learning efforts. Provide leadership to the group to support safe
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to DOE sponsors, industrial partners, and international collaborators. Basic Qualifications: PhD in Computer Science, Computer Engineering, or a field closely related to the job duties of this position. A
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, multiscale methods, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and visualization technologies