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Inria, the French national research institute for the digital sciences | Grenoble, Rhône-Alpes | France | 2 days ago
will join the Thoth project team ( https://thoth.inrialpes.fr/ ) within the Inria Centre at Université Grenoble Alpes ( https://www.inria.fr/en/inria-center-universite-grenoble-alpes ) to work under
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SEMIAMOR "Machine Learning for Column Generation" See detailed description at https://lipn.fr/~leroux/static/phd_semiamor_cg.pdfLaboratory: Laboratoire d'Informatique de Paris Nord Field of
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Application Deadline 31 Dec 2027 2027-12-31 8:00:00 2027-12-31 18:00:00 Europe/Paris Postdoc position in Machine Learning for Integrative Genomics The Machine Learning for Integrative Genomics team (https
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Your Job Develop a reinforcement learning (RL) controller for a liquid–liquid gravity settler, trained entirely offline in a simulated environment Use existing physics-informed neural network (PINN
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Your Job Develop machine-learning–based workflows and scientific software for segmentation, species classification, and lineage tracking in multi-species time-lapse microscopy data Optimize models
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consumption on complex algorithmic or cognitive tasks. This project is part of the ELEVATE MSCA Doctoral Network (https://www.elevate-dn.eu/) and co-supervised by our partners at the university of
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grant starting at the end of 2026. The research conducted by the team is broadly framed within the theoretical framework of reinforcement learning, with the aim of developing a multi-scale
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language processing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8554-YAILAK-003/Default.aspx Requirements Research Field Language sciences Education Level PhD or equivalent Research
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advances the progress of research. Your tasks involve: Developing a machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the
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Your Job In this thesis you will work in the European Space Agency (ESA)-funded project Fast-EO (Fostering Advances in Foundation Models via Unsupervised and Self-Supervised Learning for Downstream