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This position will consist of using "deep learning" methods, in particular CNNs and "transformers" for the processing of data from the IASI instrument from CNES. These observations are brightness temperature
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generate massive phenotypic datasets. We will analyze these data using deep learning to identify novel antibiotic candidates and predict their mechanisms of action. This pipeline will allow us to explore new
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approches ensemblistes ou des approches d'apprentissage profond (deep learning) qui permettent de s'affranchir de l'adjoint du modèle de chimie-transport et de traiter des très grandes quantités de données
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champ thermique de la paroi impactée. 2. Développement d'un algorithme d'apprentissage profond : Un code de deep learning sera conçu pour identifier les caractéristiques des interactions gouttes-paroi à
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urban walkability perception through hybrid sensing and Deep Learning -Macroelements in Earthquake Engineering -Experimental characterization of the sealing properties of caprock formation for CO2
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. 2024. From Sound to Sight: Audio-Visual Fusion and Deep Learning for Drone Detection. In Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '24
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/ Méthodes / Logiciels deep learning, dimensionality, AI, zero-shot classification, uncertainty quantification Profil du candidat We look for a passionate student at the end of their studies (e.g. the French
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has opened new perspectives. Neural networks, particularly deep architectures, have demonstrated remarkable capabilities in learning complex nonlinear mappings. Physics-informed neural networks (PINNs
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methods based on state-space models [3] have demonstrated strong capabilities in modeling very long sequences. In this context, these methods provide the perfect alternative to standard deep learning
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- Solides compétences en ingénierie informatique (Python, Linux, shell, git) - Expérience attendue en Computational Linguistics, NLP et en Machine Learning, Deep Learning - Connaissance des grands modèles de