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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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23 Jan 2026 Job Information Organisation/Company IMT Atlantique Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
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. Skills in computational modelling or machine learning applied to brain signals are an asset. We are looking for a highly motivated, rigorous and curious researcher who is ready to invest themselves in a
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
of computational mechanics, mechanical and civil engineering, and scientific machine learning. The postdoc will be encouraged to publish in leading international journals, present their work at major conferences
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expertise or interdisciplinary experience is a major asset. Scientific skills - In-depth knowledge of teaching strategies, learning models, and educational technology. - Proficiency in the psychology of well
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate