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engineering, applied mathematics, physics, or related field with an evidence of understanding nuclear nonproliferation and/or the nuclear fuel cycle. Experience applying machine learning techniques to problems
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treat one another, work together, and measure success. Basic Qualifications: PhD in physics, optics, electrical engineering, computer science, or a related field completed within the last 5 years
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Experience with multi-physics simulations on high performance computing (HPC) and machine learning (ML) Experience working in a multi-disciplinary research environment Demonstrated written and oral
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together, and measure success. Basic Qualifications: A PhD degree in Physics, Chemistry, Biology, Computer Science, or a related discipline A minimum of 3 years of experience in machine learning applied
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comprised of a multi-disciplinary team of scientists carrying out research to improve process understanding of the global Earth system by developing and applying models, machine learning, and computational
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completed in the last five years. Hands-on experience with machine learning, process modelling, and industrial data acquisition systems is very valuable. This position may also require access to technology
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analytics, and machine learning, the Grid Interactive Controls group delves deeply into understanding intricate grid-edge operations. Researchers are dedicated to laying the groundwork for optimal X2G
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scientists, engineers, enterprise software developers, and machine learning experts at both neutron research facilities. We are seeking applicants to study materials science & engineering using neutron
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spectroscopy/scattering techniques for the design and characterization of functional materials Knowledge and experience in application of machine learning (ML)/artificial intelligence (AI) algorithms Ability
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physics-based machine learning for land cover forecasting. Intended use of these capabilities include urban planning, hydrological modeling, and wildfire risk mitigation strategies. In addition to model