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supports the research on energy security and resilience of the power grid at the Energy Department (DOE) through advancement of modeling, simulation, and real-time monitoring of the power grid, including
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. Related fields, including hydropower and power grid modeling, hydraulic engineering, and sediment transport. The successful candidates must demonstrate an ability to work independently, evidenced by
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Materials & Manufacturing Technology Office (AMMTO) and Industrial Technologies Office (ITO) and cover the topics like smart manufacturing, additive manufacturing, carbon fiber and composites, energy-water
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, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy
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: https://www.ornl.gov/content/research-integrity Basic Qualifications: To be eligible you must have completed a PhD in chemistry, physics, engineering, or a related field with in the last 5 years
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respectful workplace – in how we treat one another, work together, and measure success. Required Qualifications: PhD in Nuclear engineering, computer science, applied mathematics, or a related field completed
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PhD in materials science and engineering, physics, chemistry, or electrical engineering or a related field. Preferred Qualifications: Experience in scanning transmission electron microscopy Background
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-flows. Basic Qualifications: PhD in mathematics, computer science, engineering, or related field earned within the last 5 years. Preferred Qualifications: Experience with mesh generation/CFD applications
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Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: PhD
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to optimize utility of captured signals Conceive, write, and submit proposals to develop and expand a research program investigating signal collection and analysis for mission objectives Qualifications: A PhD