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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
scientists developing computational simulations in various fields will be encouraged, in particular with the Gysela code, which is part of the ITER project. The project will first focus on dimensionality
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evolution of satellite communication systems, driven by the deployment of large Low Earth Orbit (LEO) constellations and the integration of non-terrestrial networks into future 6G infrastructures, is
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laboratory, and of the Computer Science Department of IMT Atlantique. The RAMBO team is studying how ambient devices and robots can develop increasingly complex skills based on active learning, artificial
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conducted to assess the feasibility of prototypes in terms of activity tracking, updating game elements, and visualisation capabilities. The second objective of the thesis is to develop models and algorithms
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energy recovery linac (ERL) demonstrator at IJCLab, Orsay. ERLs offer a promising way toward the development of future colliders, particularly by providing excellent beam quality while drastically reducing
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. The objective of this postdoctoral project is to develop a unified, AI-compatible framework for non-neural behavior based on dynamical systems and learning. Behaviors will be modeled as low-dimensional dynamical
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Discovery”, with a strong scientific and environmental ambition: developing lower-footprint AI methods for real inverse problems in nondestructive evaluation. The topic has already passed the first ENACT
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approach based on Deep Learning algorithms will be developed and implemented to obtain additional information by coupling the recorded data. Furthermore, the increase in acquisition rates of measurement
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22 Feb 2026 Job Information Organisation/Company CNRS Department Laboratoire de physique de l'ENS Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage Researcher
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of this thesis focus on several key areas: - Detection and modeling of node mobility: Supervised learning models will be developed to identify mobility patterns based on radio indicators such as RSSI, SNR, and