70 parallel-computing-numerical-methods Postdoctoral positions at Oak Ridge National Laboratory in United States
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conferences. Supporting collective team goals, working harmoniously with colleagues, and maintaining rigorous compliance with environment, safety, health, and quality program requirements are fundamental
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challenges facing the nation. We are seeking a Postdoctoral Research Associate who will support the Quantum Sensing and Computing Group in the Computational Science and Engineering Division (CSED), Computing
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separation science and technologies for energy applications. Support research and program-development activities in advanced separation and purification, including the recovery of critical materials used in
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program Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a
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meetings Ensure compliance with environment, safety, health, and quality program requirements Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact
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related field completed within the last 5 years. Experience utilizing computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data
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of using existing and emerging grid assets to ensure grid reliability and affordability of energy supplies. The group focuses on advanced grid modeling using advanced computing resources (e.g., quantum
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environment, safety, health, and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Develop strong, productive working relationships across
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publish scientific results in peer-reviewed journals on time. Ensure compliance with environment, safety, health, and quality program requirements. Maintain strong dedication to the implementation and
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computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics, including correlation analysis and machine learning techniques. Preferred