100 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"embl" positions at Oak Ridge National Laboratory
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Requisition Id 16723 Overview: We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems. This
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machine learning techniques to improve DER/IBR model calibration, parameter estimation, uncertainty assessment, and decision support. Build and apply hardware-in-the-loop test platforms to validate the full
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typical for an individual to be offered a salary at or near the top of the range for a position. Link to benefits. https://jobs.ornl.gov/content/Benefits/?locale=en_US Overview: We are seeking
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AI for science, including scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership-class supercomputers. You’ll help design
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solutions to compelling problems in energy and security. The L earning Systems Group at Oak Ridge National Laboratory (ORNL) seeks a postdoctoral researcher specializing in federated learning and
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by the Lab Director’s office can be found here: https://www.ornl.gov/content/research-integrity . Basic Qualifications: PhD degree in Mechanical Engineering, physics, or related field, or a MS
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the Safety and Operations Services Division in the Environment, Safety, Health, and Quality Directorate, at Oak Ridge National Laboratory (ORNL). Learn more about R&D activities in ISED here https
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instrument operation and troubleshooting, with opportunities to contribute to groundbreaking scientific research utilizing neutron scattering. For more information, visit https://neutrons.ornl.gov/nse
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML
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be expected to commit to ORNL’s Research Code of Conduct. Our full code of conduct and a statement by the Lab Director’s office can be found here: https://www.ornl.gov/content/research-integrity