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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
involves processing and harmonizing high-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
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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Post-Doctoral Position in Deep Learning for MRI Reconstruction at Yale University Title: Postdoctoral Associate, Yale School of Medicine Department/Division: Radiology and Biomedical Imaging
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 2 days ago
wildland-urban interfaces— across a wide range of climate conditions. Using machine learning methods, we will optimize the weightings of each contributing factor and identify the key drivers of wildfire risk
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learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 2 hours ago
. Description: Exploration to deep space will require unprecedented levels of reliability and resilience to ensure mission success. Studies have shown that a high percentage of faults that occur onboard
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application of advanced deep learning models, with an emphasis on techniques such as knowledge distillation. The candidate will engage in research involving time-series analysis, including modeling, forecasting
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. Responsibilities for this position includes:* Developing deep-learning approaches for insight and
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to systematically understand cancer biology, identify diagnostic and prognostic biomarkers, and improve cancer therapy. Projects will involve the development of AI solutions, including machine learning, deep learning
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular