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tools capable of integrating, modeling and interpreting this wealth of information. It is in this context that artificial intelligence (AI) approaches, particularly deep learning, offer considerable
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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University, is recognized for its expertise in artificial intelligence and formal methods In this stimulating academic context, we will focus on the problem of explainability of artificial intelligence models
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of external conditions. Recent work has demonstrated the strong potential of combining Internet of Things (IoT) devices with artificial intelligence in the cloud, but to date no solution has proposed embedding
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research environment at the interface of biology, chemistry, and artificial intelligence. The project is led by a multidisciplinary team specialized in cell image analysis and multimodal modeling, providing
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objective is to develop artificial intelligence methods that complement modeling techniques, particularly at the atomic scale, specific to the physical chemistry of materials. In this context
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atmospheric sciences and artificial intelligence (two PhD theses, one completed and one ongoing), and have already established a common framework ensuring effective collaboration. The appointed doctoral
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research in information and digital technologies (computer science, automation, robotics, artificial intelligence), with the aim of developing methods for representation, analysis, and control of complex
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, Heudiasyc has been closely tied to the CNRS and is affiliated with the INS2I division (Information Sciences). Its research activities span computer science, automation, robotics, and artificial intelligence
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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