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leverage state-of-the-art deep learning techniques to address challenges in visual data processing and forensic analysis. As part of a dedicated, collaborative research team, you will push the boundaries
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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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Max Planck Institute for Mathematics in the Sciences | Leipzig, Sachsen | Germany | about 16 hours ago
[map ] Subject Areas: Machine Learning/Deep Learning; Optimization, Combinatorics, Polyhedral geometry, Algebraic geometry Appl Deadline: 2025/05/01 11:59PM (posted 2025/03/19, listed until 2025/09/19
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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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Job DescriptionPosition: Postdoctoral FellowTopic: Reinforcement Learning for Vortical Flow Control and Sensing. Requirements: 1) Age under 35. 2) Ph.D. degree (obtained or about to obtain) in
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Job DescriptionPosition: Postdoctoral FellowTopic: Reinforcement Learning for Vortical Flow Control and Sensing. Requirements: 1) Age under 35. 2) Ph.D. degree (obtained or about to obtain) in
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learning. The employment is full-time for two years starting from August 1st 2025 or by agreement. Apply latest April 7th 2025. Project description Geometric deep learning refers to the study of machine
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methodologies for analysing RNA modification readouts from large transcriptomic datasets. This position will focus on developing probabilistic deep learning frameworks to identify molecular determinants
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 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