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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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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between
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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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matter and information for life. Artificial vascular networks have mimicked this strategy, without reproducing the same level of autonomy and adaptability. In the most adaptable organisms, the vascular
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be to dissect the mechanisms underlying the compartment-specific recruitment of mRNAs along axons. First, the student will dissect the contribution of activity cues. Through artificial inactivation
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of electrical topologies (AC, DC, three-phase) and will integrate both standard and atypical wear cases. On this basis, high-performance artificial intelligence models will be developed. By combining neural
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in situ (e.g., artificial substrates mimicking gravel beds, shell beds, mussel beds), with comparisons to manipulated plots differing in habitat quality. Strategy and methods - Mapping: contribution
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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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and transport EEE to the photosynthetic reaction center, where conversion into chemical energy takes place with near-unity efficiency. No artificial system has been able to approach this performance