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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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, stochastic, robust, and multi-objective optimization. Conduct analyses of industrial system resilience, competitiveness, and operational performance under uncertainty. Support model development for co
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project focused on AI-enabled resilient operation of distribution systems and networked microgrids under uncertainty, disturbances, and cyber-physical threats. This position is best suited for a candidate
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familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow. A strong foundation in statistical methods, probability theory, or uncertainty quantification is highly advantageous. Job Family Postdoctoral
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Integrate models across platforms and workflows; manage inputs/outputs and ensure reproducibility Analyze simulation and experimental datasets; extract insights and quantify sensitivities and uncertainties
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generalization to support robust analysis, interpretation, and decision-making. Apply conformal prediction and uncertainty quantification techniques to generate reliable confidence estimates and risk assessments