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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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divisions at ORNL. Major Duties/Responsibilities: Work closely with members of NTI and CNMS to develop new AI models for discovering novel permanent magnets with targeted properties using advanced concepts
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modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi
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. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
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research. Your work will focus on developing selective chelation strategies and applying these systems to targeted radionuclide therapy and cancer imaging. Research accomplishments will be disseminated
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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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targeted at the key DOE Office of Science missions. Major Duties/Responsibilities: Work closely with ORNL researchers in using the resources of the OLCF effectively and efficiently Develop and port scalable
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water utilities in setting long-term targets to significantly improve energy and water efficiency throughout their U.S. operations over a ten-year period. The ideal candidate for this role would be
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of new computational techniques specifically targeted at the key DOE Office of Science and other missions. The appointment will be for one year, with the possibility of renewal for a second year, depending