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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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calibration approaches and apply machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation
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. Demonstrated research experience with AI and ML techniques. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of
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learning potentials (iv) modeling of molecular reaction, (v) Markov-state models for kinetics, and (vi) experience in coding and HPC workflows. Knowledge of or experience in Bayesian uncertainty
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observable reconstruction. Experience with uncertainty quantification, covariance modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model
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Bayesian statistics and neural and Gaussian-process emulators for accelerating parameter estimation and uncertainty propagation Selective cross-scale evaluation using complementary ecosystem observations
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materials and/or process discovery, particularly quantum materials Some form of expertise in methods such as machine-learning force-fields for spinful materials, or multi-fidelity Bayesian models that