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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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the following ones. Exploration of active auditing techniques for large machine learning models, use of reinforcement learning, potential application to recommender systems. The PhD will mainly investigate
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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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learning. Work carried out during the Master's internship has already identified strong trends and tested statistical and machine learning approaches. The thesis will aim to consolidate and update
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will build on recent advances in machine learning for dynamical systems to extract meaningful representations of complex flame dynamics, construct prognostic ROMs, and perform data assimilation
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simulations, optimisation, machine learning and turbulence modeling. The researcher must hold a Phd in fluid mechanics / Applied mathematic / Machine Learning. Website for additional job details https
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) - Organisation - Autonomy - Interdisciplinary skills Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7252-PIEBON-004/Candidater.aspx Requirements Research FieldEngineeringEducation LevelPhD
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, autonomy, rigor and taste for teamwork Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UPR3407-ARMMIC-001/Candidater.aspx Requirements Research FieldEngineeringEducation LevelMaster Degree
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of the project (https://anr.fr/projet-ANR-24-CE28-5107 ). Main Tasks • Development of a research axis: The recruited researcher will be responsible for leading the research axis focusing on the relationship
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forces on each mode in order to reduce (i.e., cool) their individual vibrations. The student will be closely guided by the advisor and will acquire both theoretical and experimental skills on optomechanics