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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference, clustering, classification • deep learning, variational
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(History, Archeology, …). Expected skills: The candidate should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference
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Apply by sending an email directly to the supervisor Primary discipline: Machine Learning Secondary discipline: Neuroscience Project Summary This project proposes to explore how the brain and
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-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1- Context and Objective: Monitoring tumor response using clinical imaging, such as CT or FDG-PET, has become a
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/CT imaging Description of the topic As this is an interdisciplinary "AI and medicine" project, co-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1
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advances in machine learning and data-intensive approaches facilitate the search for better or even global minima via evolutionary computations or reinforcement learning. Objectives. The main scientific
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centre at Universit´e Cˆote d’Azur, I3S Lab (Universit´e Cˆote d’Azur and CNRS) in collaboration with the Machine Learning Genoa Centre (MaLGa) at the University of Genova (Italy). The candidate will be
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Angelopoulos, Stephen Bates, et al. Conformal prediction: A gentle introduction. Foundations and Trends® in Machine Learning, 16(4):494–591, 2023. Arthur P Dempster, Nan M Laird, and Donald B Rubin. Maximum
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some