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for managing smart cities. The team has gained substantial experience in machine learning for road traffic monitoring. They are now keen to thoroughly explore the additional opportunities presented by
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(I3S), Sophia Antipolis Hosting lab: I3S & INRIA UniCA Apply by sending an email directly to the supervisor: emanuele.natale@univ-cotedazur.fr Primary discipline: Machine Learning Secondary discipline
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, CNRS, I3S, Sophia-Antipolis, France) Collaboration: Luca Calatroni (Luca.calatroni@unige.it), Machine learning Genoa Center, Italy. Context and Post-doc objectives Conventional optical microscopy
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machine learning. We particularly value depth of knowledge, originality, and the potential for cross-disciplinary innovation. Relevant application areas may include (but are not limited to) natural
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(History, Archeology, …). Expected skills: The candidate should have a graduate degree (Master 2 degree). His/her scholar background should include: • statistical/machine learning, statistical inference
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unit (UMR 7248) from UCA and CNRS. Abstract Optical flow estimation is a key task in computer vision, particularly critical for autonomous navigation where accurate motion perception is essential. It can
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
. This dataset will enable the training of specialized deep learning models (neural network or transformer) for automated segmentation of tibial plateau fractures. iii) The algorithm must then be trained
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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to candidates from a broad range of AI subfields, including, but not limited to machine learning, generative AI, computer vision, representation and reasoning, natural language processing
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Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include