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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 7 minutes ago
accurately predicting risk, leveraging a framework of ensemble neural networks, CatBoost, and generative adversarial networks (GANs). The system’s predictive accuracy will then be rigorously validated against
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surrogates 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
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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 | 3 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
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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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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
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related to staff position within a Research Infrastructure? No Offer Description Call for Postdoctoral Researchers in Artificial Intelligence (AI) – Focus on Large Language Models (LLMs) for Predictive
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart
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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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intelligence and bioinformatics. The overall aim of the project is to identify diagnostic, predictive and prognostic factors in cancer to improve prevention and disease management. The main role will be