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Research axis of the 3IA: Axis 3 - AI for Computational Biology and Bio-inspired AI Supervisor (3IA Chair): Emanuele Natale, Sophia Antipolis Laboratory for Computer Science, Signals and Systems
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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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. 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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of programming, learning theory, parallel algorithms or quantum computing Research publications in theoretical computer science conferences and journals Experience in teaching Computer Science topics
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Nature Careers | Port Saint Louis du Rhone, Provence Alpes Cote d Azur | France | about 2 months ago
protein expression and purification, capable of producing thousands of proteins in parallel within weeks . 2) Eukaryotic expression systems facility for production of challenging protein targets. 3) A fully
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, dynamic and innovative researcher to integrate our community. The ideal candidate will possess deep expertise in the application of cutting edge computational methods to understand the brain mechanisms
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of several researchers working in the field of inverse problems due to their ability of combining variational inference approaches with the ability of neural networks to learn unknown posterior distributions
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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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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