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by combining scalable density functional theory (DFT) approaches (such as real-space DFT, DFTB), beyond-DFT approaches for solids (such as GW, DMFT, QMC), and reactive (machine-learning) force-field
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opportunity to creatively use interdisciplinary methods from computational data science, machine learning, geographical information sciences, and many other topics to help frame and solve the above problems on
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modeling and experiences in machine learning Strong background in modern techniques in fabrication of nano- and microfluidics An excellent record of productive and creative research as demonstrated by
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approaches for scientific data analysis and/or the latest machine learning approaches, including deep learning models. Experience working with DOE National Laboratories (or similar R&D organizations). Special
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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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aided design tools; Perform thermal and structural analyses using commercial finite element software such as ANSYS; Perform safety analyses to ensure safe experiment operation; Calculate nuclear heating
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, machine learning, sensor processing, machine vision, and/or human machine interaction. This position will require working with external partners, corporations, and sponsors to develop and deploy
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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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, physics-informed machine learning methods, and high-performance computing. We are a diverse group of engineers, material scientists, and applied mathematicians who share a common passion for developing
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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