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learning frameworks, uncertainty quantification, machine learning and inferencing, and land use/land cover characterization to inform geo-demographics. The Human Geography group supports Federal missions
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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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research staff within ADADS and throughout ORNL to develop and apply modern data science/machine learning techniques to a wide variety of subjects including nuclear material detection, nuclide identification
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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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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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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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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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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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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