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advanced Machine Learning (ML) and Deep Learning (DL) techniques. This is a four-year fixed-term contract, after which the candidate and their dossier will be evaluated by a jury for integration into
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mechanical activity at the same time. In this context, the use of mathematical models and machine learning methods can be relevant to integrate physiological knowledge in data analysis and to analyze
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, integrating machine learning and physiological computational models (patient digital twin) to: 1) combine physiological knowledge and clinical data; 2) improve model interpretability; and 3) minimize
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statistics Excellent background in statistical/machine learning Experience in computer vision is a plus Strong motivation for medical and societal applications of computational methods Knowledge of biology and
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generic approach allowing the accurate, robust, and fast simulation of the optimal fracture reduction strategy whatever the type and class of the fracture. Combined approaches exploiting both Deep-Learning
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machine learning. Significant experience in cognitive architectures and computational modeling for neuroscience, psychology, AI or cognitive robotics will be appreciated. Mastery of reinforcement learning
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Vidacs to confront optimal transportation approaches to machine learning methods.• One at UNIBO in Bologna for 12 months with prof. Daniel Reomndini to learn and apply techniques of manifold learning
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cluster computer system to realize big-data analysis and simulations. Mission confiée Context of the project Artificial Intelligence (AI) and especially Deep Learning (DL) have undergone many successes in
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a development strategy focusing on machine learning, data science modelling and artificial intelligence applied to the epidemiological prediction of future pandemics, the development of new models
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Retrieval, Patrimonial archives, Computer vision, Deep learning, recent neural architectures, ViT, CNN Research project description Some photographic archives are made up of multiple images that are almost