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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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Qualifications: A PhD in mechanical engineering, computer science, or a related field completed within the last 5 years. Preferred Qualifications: Experience planning and designing solutions for a variety of
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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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strengths in high-performance computing, system architecture, and data analytics with applications in a large variety of science domains. NCCS is home to some of the fastest supercomputers and storage systems
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in high-performance computing and data analytics with applications in a large variety of science domains and NCCS is home to some of the fastest supercomputers and storage systems in the world
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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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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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. Basic Qualifications: A PhD degree in civil, chemical, or environmental engineering. A minimum of 2 years of experience in the use of Python for programming of data analytical models and algorithms
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dynamic instabilities in materials; development of a polarization-based correlation chopper prototype; simulation of instrumentation using McStas; analytics of materials structure and dynamics using neutron