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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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, 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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3 Oct 2025 Job Information Organisation/Company CNRS Department Station d'Ecologie Théorique et Expérimentale Research Field Physics Researcher Profile First Stage Researcher (R1) Country France
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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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25 Sep 2025 Job Information Organisation/Company CNRS Department Laboratoire d'énergétique moléculaire et macroscopique, combustion Research Field Engineering Chemistry Physics Researcher Profile
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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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13 Sep 2025 Job Information Organisation/Company CNRS Department Institut Montpelliérain Alexander Grothendieck Research Field Physics Researcher Profile First Stage Researcher (R1) Country France
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optimizing a superstructure automatically generated using a deep learning approach. The project brings together the LRGP (Reaction and Engineering Laboratory, CNRS-Université de Lorraine), the LPSM
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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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Artificial Intelligence (applied mathematics, computer science, etc.), or a thesis defense scheduled for 2025. • Research contributions in deep learning, statistical learning, natural language processing (NLP