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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-STAFFFELLOW [#27209] Position Title: Position Location: Oak Ridge, Tennessee 37831, United States of America [map
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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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and other technicians with operations in chemical and radiochemical laboratories. Ensure compliance with environmental, safety, health, and quality program requirements. Mentor and train technicians
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Requisition Id 15349 Overview: The Workflow Systems Group in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a staff fellow with expertise in
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classified operations in the areas of Classified Intelligence Information Technology (IT)/Information Assurance (IA), Classified R&D Computing, and physical and personnel security in the Field Intelligence
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the National Security Sciences Directorate (NSSD) at ORNL. CRID provides new methods, tools, and strategies to detect and mitigate adversarial attacks on critical infrastructure and protections and informs
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conferences, workshops, and collaborative meetings. Explore integration of advanced computational methods with NMR data analysis, including machine learning approaches for spectral interpretation. Basic
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Isotope Program and other DOE mission areas. This has been a quickly expanding program at ORNL and offers significant opportunities for professional growth. The team demonstrates a collective excitement for
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Isotope Program and other DOE mission areas. This has been a quickly expanding program at ORNL and offers significant opportunities for professional growth. The team demonstrates a collective excitement for
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Postdoc Research Associate- Atomic-Resolution Optical Spectroscopies w/Scanning Tunneling Microscopy
). The Postdoctoral Research Associate will focus on the development of novel multimodal optical spectroscopy methods and their integration with existing STM platforms. These capabilities will enable atomic-scale