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Mathematics, Computer Vision, or Data Science. -Knowledge of statistical inference methods and machine learning. -Experience in spectroscopy and imaging is an asset. -Strong programming skills in Python
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learning techniques (RF, XGBoost, NN, SISSO) to screen all the possible compositions of the half-Heusler family in order to find high ZT materials. - Propose new ML methods. - Perform DFT calculations
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photosynthesis), in order to estimate the carbon sink/source effect linked to the cloud microbiota, and its dependence on environmental variables. The person recruited will participate in cloud sampling operations
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an entirely new study of these glossaries, offering a broader approach to their analysis in context. Highlighting their composition and dissemination makes it possible to better evaluate the working methods
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applied mathematics, physics, computer sciences or social sciences with outstanding skills in quantitative methods ; - Provable experience working with social media data, specially very large datasets (we
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the framework of the ANR EmergeNS whose aim is to understand, through mathematical and computer models, the role that autocatalysis, multistability and spatial heterogeneity may have played in the emergence
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similarity. This exploration goes beyond representations generated by computer vision and investigates broader methods for establishing dynamic and meaningful groupings of images. Such groupings may be
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phylogenetic and adaptive signals, providing a rich fossil record for evolutionary studies. This project integrates fossil and extant data using morphological phylogenetics and advanced tip-dating methods
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a collaboration between Inria and Mitsubishi Electric R&D Centre Europe (MERCE) within the FRAIME project on artificial intelligence and formal methods. The project explores, on the one hand, how
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work focused on extending methods for detecting the pose of an object (possibly occluded, even if only partially) held by a person to 360-degree robot vision, in line with mesh detection and