202 machine-learning-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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data processing and multigroup cross-section generation tools such as AMPX or NJOY. Experience applying artificial intelligence, machine learning, or surrogate modeling methods to nuclear engineering or
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. Engage with the broader community for computational methods, artificial intelligence and machine learning, and real-world coupled physics applications. Deliver on ORNL’s mission by aligning behaviors
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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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awareness and understanding of open relevant research conducted throughout the DOE complex and the world. Basic Requirements: PhD in Mechanical Engineering, Electrical or Computer Engineering, or related
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analytics, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance
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of critical infrastructure systems through AI-enabled decision support, intelligent data agents, retrieval-augmented generation, and scalable machine learning systems. This position offers a unique opportunity
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or PhD in Computer Science, Computer Engineering, Cybersecurity. Experience architecting and implementing complex distributed systems and willingness to learn Experience in cluster computing and scaling