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States of America [map ] Appl Deadline: (posted 2025/01/10, listed until 2025/07/10) Position Description: Position Description Multiphysics, Machine Learning, and Uncertainty Quantification Postdoctoral Positions Los
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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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the surrogate forward models with a Bayesian inverse modeling framework to achieve real-time or near-real-time uncertainty quantification, such that we can efficiently resolve the uncertainties rising from rock
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motivated researcher with a strong background in computational modeling, system identification, and uncertainty quantification for civil infrastructure. The successful candidate will join the Risk Assessment
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 12 hours ago
and/or issues using discretion; experience with tritium transport modelling, hydrogen in materials, or fusion blanket concepts; familiarity with data assimilation, uncertainty quantification, or large
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aims at addressing computational challenges associated with data acquisition and information extraction from complex sensors and sensor networks. Crucially, uncertainty management and quantification
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tools. However, training opportunities (e.g., via NCAS) are available for motivated candidates. Interest in probabilistic methods, ensemble simulations, or uncertainty quantification; experience is
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University, and is expected to start in early July 2025. The candidate will be responsible for the following, but not limited to: Conducting research on uncertainty quantification for thermospheric density
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other sources to train and validate AI models. Develop computational workflows incorporating LLMs, Monte Carlo Tree Search (MCTS), phylogenetic inference, uncertainty quantification, and epidemiological
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
Experience in uncertainty quantification or statistics applied to quantum chemistry and machine learning would be advantageous For more details, please take a look at the role profile. We'll still consider