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the broad field of data science to develop new data driven methodologies to assess and to improve the quality of components fabricated using Additive Manufacturing and Carbon Fiber Manufacturing processes
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our vision on how these challenges may manifest themselves in a decade or more. Our research and development focuses on cybersecurity and cyber physical resiliency, data analytics, geospatial science
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motivated Postdoctoral Research Associate to research and develop cutting-edge data reduction technologies. In this role you will work on some of the most challenging scientific problems facing the Department
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/Responsibilities: Participate in new and ongoing research in quantum communications, quantum networking, and quantum repeaters. Formulate novel quantum information solutions to problems of national interest and
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to conduct research analyses on on-site clean energy technologies. Proficiency in using Excel for engineering data analysis is essential. Must be willing to travel. You will be involved in meetings with DOE
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the Geospatial Science and Human Security Division (GSHSD) at ORNL. The group performs artificial intelligence, computer vision, and federated learning research initiatives, with emphasis on large scale geospatial
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-frequency data streams for process monitoring, and (c) practical validation of advanced control methods. The chosen candidate is expected to perform research that expands the state-of-the-art for data- and
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of climate simulations, analysis, and data transformation into relevant metrics suitable for socioeconomic climate risk analytics and decision science. The ideal candidate should have a deep understanding of
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-ray computed tomography, scanning electron microscopy, or other microscopy 3D techniques, is desirable. These modeling and microscopy approaches will be combined with experimental data to evaluate
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/NOMAD at SNS and POWDER/WAND² at HFIR, (2) Perform elastic and inelastic neutron scattering measurements on relevant powder samples under in situ conditions, (3) Combine neutron scattering data with