206 parallel-computing-numerical-methods-"DTU" positions at Oak Ridge National Laboratory
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in bridging the gap between ORNL coated particle research and development activities with industrial partners. They will help develop program plans and execute research efforts tailored to meet the
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Board of Certified Safety Professionals (BCSP) certification. Current Tennessee asbestos inspector credentials and asbestos sampling experience. Familiarity with common industrial hygiene sampling methods
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efforts across DOE scientific applications. Develop and apply distributed intelligence methods on heterogeneous computing resources from edge devices to leadership-class systems. Develop novel
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environmental, safety, health, and quality program requirements. Assist with the coordination of radioactive and hazardous waste disposal. Mentor and train technicians. Assist Group Lead with other duties as
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related activities at ORNL.Qualified applicants will have a solid foundation of Generative AI and Machine Learning skills. This position resides in the AI Operations Program office within the Application
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areas including: Quantum information sciences, Artificial intelligence and machine learning, Biotechnology, High-performance computing, Semiconductors, and Advanced materials and manufacturing. Generate
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to shape project and program development. Engage with sponsors to lead program development activities, including proposal preparation, whitepaper development, and conference participation. Mentor early
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, work together, and measure success. Basic Qualifications: A PhD in engineering, computer science, or a related field completed within the last five years. Expertise in systems dynamics and controls
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of scientific results in peer-reviewed journals and conferences. Ensure compliance with environment, safety, health, and quality program requirements. Maintain strong commitment to the implementation and
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science, or a related field with a strong focus on AI applications in manufacturing. Experience in machine learning, data science, or computational methods applied to manufacturing and materials science