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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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the Employee Relations Division of the Human Resources Directorate at Oak Ridge National Laboratory (ORNL). This role manages case intake and tracking, documentation, reporting, facilitated discussions
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/Responsibilities: Perform particle tracking simulations and computational analyses to support the design, optimization, and interpretation of advanced engineering and scientific research Develop and maintain
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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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travel planning and processing related documentation. Event Coordination: Provide end-to-end support for all ORE events under the direction of the ORE Director, including managing communications, tracking
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, and benchmarking methods that make agentic and traditional workflows reliable at scale. The group’s main objective is to empower scientists and researchers by enabling them to effectively apply
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and submission, planning, and programmatic management for SPP and ASCR research activities. The ideal candidate will be a team player who can achieve work objectives independently, accurately, and
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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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Demonstrated programming experience in modern object-oriented programming languages, preferably C++ Proficiency with collaborative software development tools and practices, such as software debugger, version