78 phd-engineer-machine-learning Postdoctoral positions at Oak Ridge National Laboratory
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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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-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), and
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Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in Mechanical Engineering, Chemical
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. Basic Qualifications: PhD in Chemistry, Materials Science & Engineering, Chemical Engineering, Polymer Science and Engineering, or a related discipline completed within the last five years and have a
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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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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: PhD in Ceramic Engineering, Materials Science and Engineering, Mechanics
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, technical reports, and presentations. Seek membership and service opportunities in professional, academic, and research organizations. Basic Qualifications: A PhD in computer science/engineering or relevant
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, reports. Seek membership in professional, academic, and research organizations. Basic Qualifications: A PhD in computer science/engineering or relevant area with an education and a research track record in
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI