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scale by developing new computer vision approaches capable of decomposing an astronomical diagram into semantically meaningful elements for analysis and editing, without relying on human-annotated
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) Familiarity with semantic technologies (ontologies, RDF, SPARQL, etc.) Experience with graph databases and triple stores: Neo4j, ArangoDB, or Apache Jena Fuseki Collaborative software development: use of Git
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experiments to investigate the diverse mechanisms (e.g attention, acoustic and semantic processing) involved when humans listen to naturalistic auditory scenes. The experiments will collect behavioral, MEG (OPM
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for linking task planning and ontology-based knowledge representation within the framework of the Humfleet project. His role will be to set up a semantic and automated representation of a fleet of heterogeneous
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generated images and videos (Deepfakes) are increasingly realistic from a vi- sual point of view. Their use to manipulate information is obvious. Several methodologies for generating semantic content exist
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have to propose mechanisms that are as generic and adaptive as possible, i.e., mechanisms that do not require precise knowledge of the semantics of the hosted application and that adapt to its load
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the support to accessing the content of graph. Methodologically, we imagine extending the previous steps to consider tasks such as contributing, maintaining, validating or semantically enriching a knowledge