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Postdoc: Geospatial AI Modelling & Uncertainty Quantification of Carbon Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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Postdoc: Geospatial AI Modelling & Uncertainty Quantification of Biomass Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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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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of the variability and uncertainty of simulated outputs • an explicit quantification of prediction error • an interpretable and controllable structure (e.g., Gaussian processes, …) 2. Model industrial system
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 38 minutes ago
computationally intensive, relying on iterative time-marching algorithms to reconstruct heating environments. This limits their use in more advanced computational analyses, such as uncertainty quantification
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Requisition Id 15253 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted
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for preprocessing, integration, and modeling of heterogeneous data (spatial, temporal, tabular) -Conduct research in explainable AI and uncertainty quantification applied to agronomic decisions. -Collaborate with
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equations, uncertainty quantification, and machine learning. Candidates must have obtained a Ph.D. in mathematics or applied mathematics before the start date and must demonstrate research excellence, strong