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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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a key tool in microscopy and for the design of sensors, because it allows for the real time quantitative monitoring of several environmental or biological parameters simultaneously. Multiplexing is
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ExperienceNone Research FieldPhysicsYears of Research ExperienceNone Research FieldTechnologyYears of Research ExperienceNone Additional Information Eligibility criteria communicating objects, wireless sensor
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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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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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the Quantitative Imaging Platform of Villefranche (PIQv; https://sites.google.com/view/piqv ), which oversees the operation of the tools that the team develops. Those tools include imaging sensors, such as the
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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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(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
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Additional Information Eligibility criteria Physics, applied physics, engineering sciences, optics and/or MEMS technologies. Experience with atomic/quantum clocks, sensors and metrology is a strong plus-value
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 24 days 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