Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- KINGS COLLEGE LONDON
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Aalborg Universitet
- Aalborg University
- FAPESP - São Paulo Research Foundation
- ICN2
- Japan Agency for Marine-Earth Science and Technology
- Leibniz
- Linköping university
- National Aeronautics and Space Administration (NASA)
- University of North Carolina at Chapel Hill
- 1 more »
- « less
-
Field
-
at Linköping University, where we laid the foundation for recent breakthroughs in protein structure prediction, which was later awarded the 2024 Nobel Prize in Chemistry. Since then, we have further developed
-
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
-
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
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days 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
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours 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
-
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
-
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
-
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
-
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
-
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