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dozen input variables). The foreseen approach would be to build on recent developments in using CNNs for Species Distribution Models (e.g. Deneu et al 2021, Morand et al 2024) to summarise the complex
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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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. 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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such as distributed generation, interconnection, interaction among local grids and storage, with a particular emphasis on determining exchange prices for energy resources that can stimulate the development
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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 26 days ago
Conference on High Performance Computing, Data, and Analytics (HiPC 2017). https://legion.stanford.edu/pdfs/hipc2017.pdf Visibility Algorithms for Dynamic Dependence Analysis and Distributed Coherence. Michael
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between the simulator and sensors located in the operating room. This will require implementing data acquisition algorithms and data processing algorithms within the simulator. The second point concerns
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? • comment se comportent-elles face à des données hétérogènes (par exemple, personnalisées ou dont les distributions statistiques ne sont pas identiques dans tous les ensembles de données), ce qui est fréquent
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 2 months ago
distributed optimization with noisy communication channels, accurately study the selected algorithms, participate in the development and maintenance of the PEPit (https://pepit.readthedocs.io/ ) software
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learn a monolithic, “black-box” world model, often using a large neural network as function approximators. While these models can be highly effective for prediction within their training distribution