162 high-performance-computing positions at Oak Ridge National Laboratory in United States
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) at Oak Ridge National Laboratory (ORNL) is seeking a Research Associate to perform R&D in the areas of bulk power systems electromagnetic transient (EMT) simulations, high-fidelity dynamic and transient
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will be part of a dynamic team within the National Security Sciences Directorate, working alongside experts in data science, statistics, nuclear engineering, and scientific computing to support high
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Program and specifically associated with the rapidly growing field of stable isotope enrichment. Major Duties/Responsibilities: Develop, research, and execute methods of processing materials unique
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, Computer Science, or a closely related field. Experience in at least one of the following areas: FPGA programming (VHDL/Verilog, HLS) Pixel detectors in high-energy physics or radiation detection
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-performance computing (HPC) environments. Experience collaborating with national laboratories, industry, or government agencies. Strong communication skills and ability to work effectively in embedded, domain
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, published specifications, and industry standards. This position plays a critical role in maintaining reliable, safe, and high-performing systems in support of ORNL’s mission. This position resides in
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, and disposition of various radioactive waste streams. Keep work areas in a safe, clear, and orderly condition. Perform other related duties as assigned by the BVO managers/supervisors. Ensure compliance
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Requisition Id 15945 Overview: The Oak Ridge National Laboratory has an opening for a Painter. In this position, you will perform, under limitedsupervision, a wide variety of painting jobs utilizing
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crystal material’s growth and characterization. You will perform cutting-edge research on theory and modeling of dynamics in condensed matter. Major Duties/Responsibilities: Development of theoretical
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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