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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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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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prioritization. By explicitly combining ecology, economics, and decision theory, EcoDisco seeks to produce methods that are robust, policy-relevant, and sensitive to the deep uncertainties inherent in conservation
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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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, 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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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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for automatic process generation. Generative approaches, using deep learning algorithms, can generate new process structures, surpassing conventional optimization techniques. Objectives of the ATHENA project
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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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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