182 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Oak Ridge National Laboratory in United States
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Requisition Id 15517 Overview: The National Center for Computational Sciences (NCCS) at Oak Ridge National Lab (ORNL), which hosts several of the world’s most powerful computer systems, is seeking
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operations. Develop AI-powered digital twin frameworks that unify data acquisition, simulation, and control—forming the foundation of self-aware nuclear testbeds within ORNL’s LOTF digital infrastructure
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cell processing equipment Prepare clear and accurate documentation (check sheets, data sheets, logs, forms) for any work done Keep work areas in a safe, clear, and orderly condition Perform other related
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3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division, Computing and Computational Sciences Directorate
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at ORNL. Research activities will include the design of efficient data preprocessing workflows, transforming level-1b large volumes of high-resolution satellite imagery, deployment feature extraction and
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framework for driven and open quantum systems. Phenomenological modeling of dynamics/transport behaviors in complex systems, including strongly correlated electron systems. Experience in analyzing data from
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in computer science, computer engineering, information systems, science, business, or a related field of study and a minimum of five (5) to seven (7) years of aligned professional experience is
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Starlink. Manage backup services and ensure data availability. Maintain facility camera systems and support handheld radios and mobile devices. Assist users with desktop and instrument issues across
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level technical problems. Supporting all aspects of iOS and mobile devices (configuration, troubleshooting, special application uses, etc.) Resolving computer and technical problems using various means
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science