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the exact calculation of the square-root and inverse square-root of the source distribution covariance matrix. This approach offers analytical and computational advantages in comparison to existing methods
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 6 hours ago
21 Aug 2025 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile First Stage Researcher (R1
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The candidate will work at the
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Leveraging the spatio-temporal coherence of distributed fiber optic sensing data with Machine Learning methods on Riemannian manifolds Apply by sending an email directly to the supervisor
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learning, focusing on identifying abrupt shifts in the properties of data over time. These shifts, commonly referred to as change-points, indicate transitions in the underlying distribution or dynamics of a
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statistics and machine learning, focused on identifying abrupt shifts in the properties of data over time. These shifts, known as change-points, indicate transitions in the underlying distribution or dynamics
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data-silos, like hospitals that cannot share their patients' data [4]. Research goal: One of the main scientific challenges of FL, in comparison to other forms of distributed learning, is statistical
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the ability of neural networks to learn unknown posterior distributions distributions. Their use in the field of image microscopy, however, remains limited. The purpose of this PhD thesis is to develop
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. Processing this response provides estimates of the local variations in acoustic pressure along the fiber, over distances ranging from 40km up to 140km with some systems. This technique, called Distributed
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fields for several applications in the field of computer vision and inverse problem [SLX+21]. As far as the modeling of data term between distributions is concerned, one idea would be also to follow