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Field
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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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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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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 | about 6 hours 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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 25 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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science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM fields. Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep
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-accelerated iterative optimization strategies and deep learning-driven approaches. We are looking for a candidate with an interest in applying optimization and machine-learning techniques to solve real-world
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is available in the exciting field of mathematics of deep learning, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded research focuses on a
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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NASA. The researcher will utilize multi-decadal satellite imagery and deep learning techniques to analyze temporal trends in urban structure and their impacts on microclimate, focusing on extreme heat