74 phd-computer-artificial-machine-human Postdoctoral positions at Oak Ridge National Laboratory
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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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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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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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publish scientific results in peer-reviewed journals in a timely manner Ensure compliance with environment, safety, health, and quality program requirements Maintain strong dedication to the implementation
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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
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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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Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: Candidates must have a PhD in nuclear
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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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peer-reviewed journals in a timely manner. Ensure compliance with environment, safety, health and quality program requirements. Maintain strong commitment to the implementation and perpetuation of values
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte