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, the post-doctoral fellow will consider designing distributed learning algorithms for streaming manifold-valued data. Experiments will be carried out on urban, coastal, and underwater DAS data. The novelty
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frameworks, as they impose minimal, if any, assumptions about the underlying data distribution, making them more effective for detecting a wide range of changes. The CPD algorithms will be designed
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. The monitoring of telecommunications and energy production and distribution networks are characteristic examples of such time-critical applications. The project aims to propose unsupervised online CPD algorithms
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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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heterogeneity, i.e., the fact that clients' local datasets are in general drawn from different distributions. Statistical heterogeneity for example slows down the convergence of FL algorithms [5]. In this thesis
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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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. The HEXAPIC project aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimizing PIC algorithms
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, computer science, or a related field is required, together with relevant expertise in areas related to Artificial Intelligence, such as: Foundational Models, Algorithmic Research Machine/Deep Learning Computer