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of Aalborg University. Job Description This position is part of the cross-disciplinary DK-Future project – Probabilistic Geospatial Machine Learning for Predicting Future Danish Land Use under Compound Climate
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Learning for Predicting Future Danish Land Use under Compound Climate Impacts, funded by the Villum Foundation (Synergy Programme). Two postdoctoral researchers will collaborate across AAU’s Departments
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
prediction using large-scale multimodal neuroimaging data. The research will emphasize methodological innovation in statistical modeling, transfer learning, machine learning, model ensemble strategies, and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 33 minutes ago
-resolution NASA EO data. Subsequently, we will architect and train an ensemble of deep learning and statistical models capable of identifying key wildfire drivers and accurately predicting risk, leveraging a
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computational processes, improving prediction accuracy, and enabling the creation of extensive model ensembles at a reduced cost. In this context, we are looking for a highly motivated postdoctoral researcher
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description] Topic 1: Multi-model ensemble prediction of weather, sub-seasonal to seasonal climate variability (one position). Constructing a seamless atmospheric forecasting system with a large number of
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to anthropogenic climate change. Nevertheless, these extreme events may be modulated by large-scale climate variability modes across a wide range of spatial and temporal scales. Using large ensemble multi-model
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of electronic Hamiltonians. The postdoctoral researcher will develop graph neural networks based on the MACE architecture to predict Hamiltonian elements for 2D materials and van der Waals heterostructures, with
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and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform the Intergovernmental Panel on Climate Change
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) Developing strategies for decadal predictions of the Baltic Sea climate Analysing the skill of decadal predictions Analysing large ensembles of multi-decadal scenario simulations Investigating natural climate