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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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. Experimental characterization of Hall effect thrusters using combination of diagnostic techniques such as optical emission and absorption, Langmuir probes, etc. enhanced by the application of machine learning
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» AlgorithmsYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in one of the following areas (or related fields): * Machine learning / deep learning * Quantum computing / quantum
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, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology
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of autonomous mobile machines integrating perception, reasoning, learning, action and reaction capabilities. The team's main research areas are: architectures for autonomous robots, human-robot interaction
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of massive galaxies from the primordial Universe to z~2. This project combines a unique JWST dataset with state-of-the art hydrodynamical simulations and machine learning techniques to understand the origins
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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that interaction represents the foundation of active learning and fosters acquisition and retention of knowledge, as opposed to passive reception in traditional teaching. Some benefits of MR are now well established