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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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order model, machine learning, data-driven algorithms, deep reinforcement learning The Pprime laboratory is a CNRS Research Unit. Its scientific activity covers a wide spectrum from materials physics
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of computer vision and machine learning Proficiency in English (oral and written) Experience with Deep Learning is a plus To Apply: Please send a long CV, motivation letter, and academic transcripts to Prof
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for Artificial Intelligence. https://miai.univ-grenoble-alpes.fr/ Activities Develop and evaluate deep learning tools for MRI fingerprint data Write scientific articles Present results at international conferences
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from renewable electricity and sustainable raw materials, represent a promising solution, enabling deep decarbonization. DESIRE is a Marie Sklodowska-Curie doctoral network that aims to train 15 doctoral
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
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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-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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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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) 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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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