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-Powered Q&A Systems for Multimedia, 2024, p. 36-43. [6] A. F. Wise, S. Knight, et S. B. Shum, « Collaborative Learning Analytics », in International Handbook of Computer-Supported Collaborative Learning, U
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SUBATECH. The proposed thesis is part of the research activities of the team RAMBO of the RAMBO team (Robot interaction, Ambient systems, Machine learning, Behaviour, Optimization) of the Lab-STICC
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 9 days ago
) Optimization and parameter identification methods Data-driven modeling and machine learning Physics-Informed Learning (or hybrid modeling approaches) Handling and analysis of large-scale datasets (e.g., mobility
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: multilevel models for longitudinal EMA data, extraction of characteristics/features from physiological data (signal processing), as well as modeling in machine learning. # Data Management and Structuring
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des algorithmes d'apprentissage automatique (IA et Machine Learning) pour prendre en compte les phénomènes de dérives et permettre l'auto calibration des mesures
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. The successful candidate will be employed at the Department of Computer Science of the University of Luxembourg and have access to high-performance computing resources suitable for large-scale machine-learning and
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 16 days ago
Computer Science, Machine Learning, Bioinformatics, Computational Biology, or related fields. Strong experience in deep learning, ideally with PyTorch. Proven experience with graph neural networks, geometric deep
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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resources of CESAM, including its Machine Learning and Deep Learning hub, • close collaborations with ONERA. The successful candidate will work in a multidisciplinary environment bringing together researchers