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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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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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throughput genomics that informs advanced numerical analysis methods (modeling, statistics, machine learning). Plankton encompasses all organisms roaming with marine currents. Those organisms are responsible
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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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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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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
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structures of the Universe, the distant Universe and advanced statistical methods. Funding will be provided for travels and for computer equipment. The applicant will have access to two high-performance
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throughput genomics that informs advanced numerical analysis methods (modeling, statistics, machine learning). Plankton encompasses all organisms roaming with marine currents. Those organisms are responsible
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of donor/recipient (D/R) epitope compatibility using two methods. HLA-Epicheck does not allow quantification of structural differences between two epitopes. The first option is to use the descriptors
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in the Earth's outer core, with implications for deep Earth processes [1]. A variety of inverse methods (data assimilation, machine learning, etc.) has been employed to recover the fluid motions in