171 machine-learning "https:" "https:" "https:" "University of St" positions at Oak Ridge National Laboratory
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competing structural phases and the vibrational and electronic structure in materials with defects and disorder. This effort will further seek to implement strategies to leverage machine learning techniques
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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modeling, multiscale approaches) to support materials development and manufacturing process understanding. Use AI, machine learning, and data-driven methods as enabling tools to accelerate experimentation
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informed of class logistics. Use the Learning Management System (LMS) for monitoring registrations, determining class assignments, setting delivery schedules, printing rosters, and verifying training records
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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strength of ORNL which is the U.S. Department of Energy’s largest Office of Science laboratory. ORNL is home to two user facilities in manufacturing – the Manufacturing Demonstration Facility (https
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fabrication Machine learning (ML)/artificial intelligence (AI) coursework Experience with AI/ML libraries (TensorFlow, PyTorch) Special Requirements: Work involves various physical requirements and working
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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 Basic Qualifications
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product definition data using computer-aided design (historically Creo). Identify and resolve model, design, and interface problems together with system lead engineers. Provide insights for designing