267 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Oak Ridge National Laboratory
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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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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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environmental conditions, and predicting photosynthesis at multiple scales. The selected postdoctoral scientist will work with a team of mathematicians, computational scientists, plant geneticists and
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Qualifications: Ph.D. in electrical engineering, computer science, or related discipline completed within the last five years. Demonstrated expertise in computed tomography (CT), with experience in sparse-view and
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team with vendors and both internal and external collaborators and partners. Basic Qualifications: BS degree in computer science or closely related field and a minimum of 8 years as a Platforms engineer
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science, computer science, computer engineering, electrical engineering, and optical engineering, and frequently collaborates with partners in industry, academia, and other government organizations
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transformative solutions to compelling problems in energy and security. The Enrichment Systems Engineering Section is seeking a Senior Machine Design Engineer who will support the Enrichment Science and
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program managers. Represent the Accelerator Science and Technology section to RAD, NScD, ORNL, and the broader external community. Support the mission of the Accelerator Institute to expand the application
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security challenges facing the nation. We are seeking a Machine Learning (ML) Research Engineer who will support the development of self-supervised learning methods for large vision-language models
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://www.ornl.gov/facility/mdf ) and the Carbon Fiber Technology Facility (https://www.ornl.gov/facility/cftf ) – as well as signature user facilities in high performance computing, neutron sciences, and other areas