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this goal, it is paramount to characterize the added value of using machine learning in estimating and decoding quantum errors occurring in coded quantum systems. Research program: The PhD student will first
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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machine learning with the logical reasoning and semantic understanding of symbolic AI (often referred to as material and design informatics) is being developed for the accelerated discovery and development
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. Experimental characterization of Hall effect thrusters using combination of diagnostic techniques such as optical emission and absorption, Langmuir probes, etc. enhanced by the application of machine learning
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20 Sep 2025 Job Information Organisation/Company CNRS Department Institut d'électronique, de microélectronique et de nanotechnologie Research Field Engineering » Materials engineering Physics
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of machine learning algorithms are of real interest in improving the accuracy of water quality measurements, particularly in identifying, accounting for, and neutralizing ionic interference. The second key
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4 Oct 2025 Job Information Organisation/Company CNRS Department Heuristique et Diagnostic des Systèmes Complexes Research Field Engineering Computer science Mathematics Researcher Profile First
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optimizing a superstructure automatically generated using a deep learning approach. The project brings together the LRGP (Reaction and Engineering Laboratory, CNRS-Université de Lorraine), the LPSM
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. The project proposes an innovative approach to model sea ice dynamics from the ice floe scale to the basin scale, leveraging hybrid data assimilation and machine learning methods to shape a physically robust
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(Technology for engagement and personalized support of people with aphasia in rehabilitation). The candidate will join a research team with extensive experience in ergonomics and HCI, particularly in the design