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engineering, chemical engineering, or a related field completed within the last five years Quantitative and analytical research background which can be applied to conduct research analysis on advanced
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/ML techniques for real-time, real-world data Transport and dispersion modeling Fate modeling of materials in the atmosphere Applied statistics Data analytics Deliver ORNL’s mission by aligning
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Department of Energy (DOE). ORNL’s CCP conducts world-class research and development in multi-scale computational coupled physics, large scale data analytics and DL, and model-data integration at the DOE’s
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data analytics using tools in programming languages such as Python, PyTorch, Pandas, Scikit Learn, etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient
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breeding blankets, including computational fluid dynamic (CFD), thermal hydraulic, and magnetohydrodynamic (MHD) analyses. We seek individuals with advanced analytical and computational skills who can use
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completed within the last 5 years or to be expected in 2025. Demonstrated experience working with machine learning and data analytics using tools in programming languages such as Python, PyTorch, Pandas
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Requisition Id 15490 Overview: The Multimodal Sensor Analytics group in the Electrification and Energy Infrastructure Division (EEID) is seeking a postdoctoral researcher with proven expertise in
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, data analytics, geospatial science and technology, nuclear nonproliferation, and high-performance computing for sensitive national security missions. We also enhance ORNL contributions to national
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Requisition Id 15489 Overview: The Analytics and AI at Scale (AAIMS) group under Advanced Technology Section (ATS) of NCCS is hiring two postdoctoral research associates to push the frontier
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critical to understanding target behavior during irradiation in the High Flux Isotope Reactor (HFIR). This could also involve development, validation, and deployment of new physical and analytical testing