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for building system dynamics models and for converting those models into probabilistic programs so their parameters can be inferred from data. The successful candidate will extend the mathematical and
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will develop genetic engineering platforms for diverse non‑model microbes, including aerobic and anaerobic community members for microbiome engineering and organisms capable of converting waste plastics
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, patents, journal papers, and conference publications, participate in proposals, and estimate costs. Basic Qualifications: A PhD degree in physics, optical or electrical engineering, nuclear engineering, or
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, mechanical engineering, computer science, electrical engineering, industrial and systems engineering, operations research, robotics, data science, or a closely related field. Demonstrated research experience
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. To conduct this work, the successful candidate will use the world's first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric grid analytics, and
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– in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in physics, optics, photonics, applied physics, electrical engineering, or a related discipline, earned no
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, and measure success. Basic Qualifications: PhD in Plasma Physics, Electrical Engineering, Nuclear Engineering, or a closely related field completed within the last 5 years, or expect to complete a PhD
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. Represent ORNL and the Learning Systems Group by giving talks at public forums (conferences, workshops, guest lectures, etc.). Basic Requirements: Ph.D. in Computer Engineering, Electrical Engineering
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Postdoctoral Research Associate - Multifunctional Equipment Integration Energy Conversion Technology
in mechanical, chemical, materials, or electrical engineering, or a related discipline obtained within the last five years. A minimum of 3 years of experience in (a) design and operation of
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected