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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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Senior R&D Staff - Physics-informed Machine Learning Scientist for Autonomous Self-driving Laborator
Requisition Id 12780 Overview: We are seeking a Machine Learning Scientist who will focus on research and development of new physics-informed machine learning algorithms, as well as writing and
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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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research experience beyond the PhD. A well established track record of research in an area relevant to one or more areas of expertise of the group. Knowledge of state-of-the-art machine and deep learning
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to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation. We are seeking a Machine Learning Scientist who will focus on research and development
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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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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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with machine learning, particularly as applied to hydrology or other environmental systems Experience developing software for, and running software, in a cluster computing environment Experience with
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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