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Postdoc Research Associate - Machine Learning/NLP for PEM Fuel Cell & Electrolyzer Materials Develop
Laboratory (ORNL) invites applications for a Postdoctoral Research Associate. This position focuses on the integration of machine learning and natural language processing (NLP) techniques in the development
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Power BI development and a proven foundation in Data Science, including fundamental-level skills in Generative AI and Machine Learning! This role focuses on designing, developing, and moving BI, Analytics
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Power BI development and a proven foundation in Data Science, including fundamental-level skills in Generative AI and Machine Learning! This role focuses on designing, developing, and moving BI, Analytics
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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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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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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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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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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