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learning solutions that support laboratory-wide training programs and organizational performance initiatives. This position resides in the Instructional Systems Design Group within the Office of Technical
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Demonstrated experience in control theory and control system design (e.g., model predictive control, feedback control, adaptive control) Experience applying machine learning/AI to engineering systems Proficiency
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. Design, implement, and experimentally validate advanced learning and control strategies for power electronic converters and grid-interactive systems. Develop embedded control algorithms using DSP, FPGA
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to apply to join the team as a Senior High Performance Computing (HPC) Engineer for Classified Computing to lead the design, implementation, and management of HPC systems within a classified environment. We
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, machine learning, mobile robotics, process control, sensor processing, machine vision, and/or human machine interaction. This position will require working with external partners, corporations, and sponsors
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mentor junior staff on chemical processes and provide guidance on historical processes, prior trials, and lessons learned. Support the development of reports, presentations, and operator aids or guidelines
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thinking, strategic planning and execution skills. Must be able to learn ORNL financial and procurement systems and use them at an expert level. Ability to function well in a fast-paced research environment
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-Performance Computing (HPC) Engineer for Classified Computing to lead the design, implementation, and management of HPC systems within a classified environment. We are looking for candidates with extensive
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Requisition Id 17039 Overview: We are seeking a Nuclear Material Control and Accountability Specialist who will focus on supporting the Oak Ridge National Laboratory (ORNL) NMC&A Plan. ORNL NMC&A is
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worldwide challenges. Our research and development capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning