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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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research group in computer vision and machine learning, with seminal results in 3D reconstruction from images, scene understanding, deep learning, optimization, sparsity, etc. IMAGINE is part of
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and guide policy insights; ● Publishing methodological and applied findings, and presenting at international conferences. Your PhD will thus lie at the interface: you'll not only build mathematical
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, data-driven algorithms, deep reinforcement learning The Pprime laboratory is a CNRS Research Unit. Its scientific activity covers a wide spectrum from materials physics to mechanical engineering
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renewable electricity and sustainable feedstocks, represent a promising solution, enabling deep decarbonization. DESIRE is a Marie Sklodowska-Curie Doctoral Network aiming to train 15 PhD researchers in
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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-informed deep learning is rapidly advancing, integrating artificial intelligence with the governing physical laws to achieve more faithful representations of atmospheric processes. In the field of remote
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closely with our collaborators to establish a deep learning-based image analysis pipeline. The successful applicant should hold a PhD in cell biology or neuroscience. Previous experience in live cell
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influence helps detect labeling errors or prioritize unlabeled images, optimizing the learning algorithm and service quality. The doctoral student will carry out their work at IMAG (UMR of Mathematics) and
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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will