99 distributed-algorithm-"Newcastle-University"-"Newcastle-University" Postdoctoral positions in France
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algorithms that are robust to prediction errors, while still performing well when the prediction is accurate. Where to apply E-mail job-ref-o6ncfux8o1@emploi.beetween.com Requirements Research
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: Computer science MAIN SUB RESEARCH FIELD OR DISCIPLINES1: Distributed Algorithms – Fault Tolerance – Cloud computing JOB /OFFER DESCRIPTION We are looking for a young researcher interested in fault tolerance and
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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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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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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
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are looking for a postdoctoral researcher in the field of image generation algorithms (specializing in Deep Learning) for a 12-month position. The work will take place within the IMAGE research team
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such an environment and using this information in control algorithms to ensure robust navigation. This project is funded by the Hauts-de-France Region. Mission The recruited person ensures research work. She will be in
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4 Oct 2025 Job Information Organisation/Company CNRS Department Laboratoire d'informatique de modélisation et d'optimisation des systèmes Research Field Computer science Mathematics » Algorithms
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 1 hour ago
. Test model predictions in behavioral experiments. Investigate how principles of biological adaptability can inform the design of efficient and robust learning algorithms for artificial systems
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Description Evaluation of the robustness of ML algorithms executed on a non-hardened accelerator through circuit-level fault-injection The project addresses the challenges of deploying AI applications