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, https://hal.science/hal-04930868 . [2] Peyré, G., Cuturi, M., et al. (2019). Computational optimal transport: With applications to data science. Foundations and Trends in Machine Learning, 11(5-6):355–607
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within a coherent computational model is currently challenging, due to the typical large dimension and complexity of biomedical data, and the relative low sample size available in typical clinical studies
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Context Conventional optical microscopy techniques, such as confocal microscopy, are widely used in biology for investigation on cellular and sub-cellular structures. However, their spatial
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has a wide range of applications in domains focused on monitoring and securing complex systems, including mobility, manufacturing, communication, economics, and environmental science. These domains
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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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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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this technology for capturing and processing the pulse of a city. In particular, in many applications, DAS suffers from the multi-source aliasing problem and low-frequency noise, especially in noisy environments
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machine learning or theoretical biophysics or computer science Conditions. Postdoc position funded by Centre Inria at Université Côte d’Azur, France. The position is located in Sophia-Antipolis, French
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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
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or equivalent in data science, signal processing or applied mathematics and will require a strong background in theoretical as well as computational aspects of linear algebra, optimization and signal processing