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, too computationally demanding for real-time use, and lack reliable uncertainty quantification. The methods developed in the project will tackle these shortcomings, enabling computationally efficient
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lack reliable uncertainty quantification. The methods developed in the project will tackle these shortcomings, enabling computationally efficient inference and prediction of gas dynamics at high spatial
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, MATLAB, R, or Julia). Curate and integrate experimental data to calibrate and validate models, including parameter estimation and uncertainty quantification. Work collaboratively with experimentalists and
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and power grid analysis, and uncertainty quantification for engineered systems. Collaboration with experts from various scientific disciplines and applications, and following team planning, goals and
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improved performance in tasks of systems analysis like parameter estimation, solving inverse problems, and uncertainty quantification. The successful candidate will join a multi-institution research team
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statistics, causal inference, uncertainty quantification, fairness and interpretability in AI. We encourage interdisciplinary research, integrating statistical innovation with decision sciences, healthcare
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statistics, causal inference, uncertainty quantification, fairness and interpretability in AI. We encourage interdisciplinary research, integrating statistical innovation with decision sciences, healthcare
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through advances in statistical and mathematical AI and ML theory and algorithms. Examples of topics of interest include: statistically-principled methods for uncertainty quantification; operator learning
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statistical methods for big data—including, but not limited to, high-dimensional data analysis, statistical machine learning, computational statistics, and uncertainty quantification—with applications in areas
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scientific computing or programming (e.g., Python). Knowledge of uncertainty quantification or inverse problems is welcome but not required. You have good verbal and written communication skills. You are