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-sensing data, hydrologic and environmental measurements, and advanced computational models. These integrated data and analytical capabilities provide critical insights into how ecosystems respond
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, industrial engineering, electrical engineering, environmental
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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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manufacturing competitiveness analysis relevant to advanced manufacturing technologies. The team builds and applies analysis tools and analytics to draw insights on the manufacturing sector’s impact on energy
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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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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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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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. Design and demonstrate connected-lab architectures for nuclear R&D by leveraging ORNL’s state-of-the-art testbeds and capabilities in robotics, simulation, data analytics, and real-time sensing. Drive
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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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and technical professionals on new methods to advance GeoAI for end-to-end multi-modality geospatial data analytics. Deliver strong science and engineering artifacts demonstrating research innovation