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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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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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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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expertise in methods such as machine-learning force-fields for spinful materials, or multi-fidelity Bayesian models that can learn machine-learning force-fields along with effective spin Hamiltonians from ab
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materials to meet scientific objectives Debug and profile applications for high performance Conduct research and report results in journal publications, conference papers and technical manuals Basic
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land loss and vegetation change at high spatial resolution Work closely with remote-sensing scientists, modelers, and empiricists across DOE laboratories and universities to address project objectives
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concentration analyzer (G2508), Rhizovision Explorer, Shimadzu TOC-L, ICP-MS/OES, and Aqualog for EEMS/UV-VIS. Work closely with modelers and empiricists across multiple disciplines to address project objectives
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into nuclear forensics applications. The Materials & Chemistry Group focuses on fundamental chemical and physical science related to nuclear fuel cycle materials in support of national security objectives. MCG