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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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policies and procedures. Must be familiar with government procurement terminology. Experience applying and demonstrating procurement knowledge in solving a wide range of complex problems. Learning to lead
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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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laboratory investigations with inorganic mercury and methylmercury. Acquire and analyze data using a range of analytical instrumentation. Maintain detailed and accurate records. Prepare oral and written
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, management assessments, event reporting, and lessons learned to support business operations and management system performance improvement. Interface with other Battelle and DOE National Laboratories
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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within and outside the laboratory. Initiative and flexibility to work varying schedules, including shifts starting as early as 7:00 a.m. Willingness and ability to learn and perform various tasks, adapting
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Suite (Word, Excel, PowerPoint, Outlook) and Adobe Acrobat. Ability to learn ORNL business systems. Strong interpersonal and communication skills to interact tactfully with staff and external visitors