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of cryptography is required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications should include a CV, a list of publications and a research statement. Applicants should
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 16 days ago
), the objective of this internship is to improve the performance of full waveform inversion (FWI) algorithms for seismic subsurface imaging. This work lies at the interface between Earth sciences, mathematics, and
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» Algorithms Researcher Profile First Stage Researcher (R1) Country France Application Deadline 27 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1
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algorithmic consequences. Topics of interest include coarse equivalents of classical graph theorems and parameters, asymptotic minors, and coarse embeddings; Where to apply E-mail job-ref-5sp0nv8qvb
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model characterisation strategies definition of the necessary equipment definition of the necessary processing algorithms Implementation/validation/drafting of scientific articles in collaboration with
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(MCs) for civilian ships or military vessels requires robust and efficient in-situ waves-based Structural Health Monitoring (SHM) algorithms to monitor them throughout their lifetime in a harsh
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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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Is the Job related to staff position within a Research Infrastructure? No Offer Description The researcher will use and further develop ptychography algorithms for transmission electron microscopy data
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26 Nov 2025 Job Information Organisation/Company CNRS Department Institut de Recherche en Informatique de Toulouse Research Field Computer science Mathematics » Algorithms Researcher Profile First
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop