202 machine-learning-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory
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of AI for science such as: scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership-class supercomputers. You’ll help design, train
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Requisition Id 17081 Overview: We are seeking a Beam Instrumentation Physicist to support the design, development, implementation, and maintenance of beam instrumentation computer systems
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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. in Quantum computing, Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a closely related discipline, with demonstrated knowledge of or research experience in
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challenges. Our research capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning (AI/ML), neuromorphic sensing
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challenges. Our research and development capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning (AI/ML
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and Machine Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML models
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export control laws and regulations. Identify high risk property, including items that are proliferation-sensitive, export controlled, military/weapons related, or specially designed or prepared
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dark-field STEM imaging, energy dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy, at the intersection of electron microscopy, software engineering and machine learning. Major
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and