366 computing-"https:"-"IDAEA-CSIC"-"https:" positions at Oak Ridge National Laboratory
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Requisition Id 16991 Overview: The Field Intelligence Operations Division (FIOD) of the National Security Directorate (NSSD) is seeking a Senior Engineer for a Classified High Performance Computing
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collaborative research portfolio. This position resides in the Defense Manufacturing Program Office (DMPO) in the National Security Science Directorate, NSSD at Oak Ridge National Laboratory (ORNL). The National
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Engineer will conduct R&D in nuclear nonproliferation with expertise in computational nuclear reactor physics through modeling and simulation (M&S). The candidate will perform analysis and methods
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for Computational Sciences (NCCS). The group creates and deploys workflow, data, and AI-agent technologies that connect leadership-class computing with experimental and observational science. Its primary goal is to
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Requisition Id 16981 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 a
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Requisition Id 16980 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 a
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the design, evolution, and modernization of secure computing environments supporting some of the nation’s most consequential national security, intelligence, and scientific missions. This is more than a
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Requisition Id 17081 Overview: We are seeking a Beam Instrumentation Physicist to support the design, development, implementation, and maintenance of beam instrumentation computer systems
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Requisition Id 17094 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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computational foundations of that capability and help bridge the gap between Bayes theory and practical application: knowledge integration, developing robust likelihood frameworks, sampler behavior for long