199 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory
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and approaches to solve complex problems (e.g., information retrieval/extraction, machine learning/deep learning, networking) Experience working with geospatial data and processing workflows and
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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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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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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. 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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a minimum of 5 years of relevant technical experience. An equivalent combination of education, military technical training, and relevant technical experience may be considered. Ability to perform
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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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learning solutions that support laboratory-wide training programs and organizational performance initiatives. This position resides in the Instructional Systems Design Group within the Office of Technical
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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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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