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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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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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expertise in developing computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023
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surrogate models and experimental data from a pilot-scale settler are available. The project offers close supervision from doctoral researchers in machine learning and process control. Your Profile
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Inria, the French national research institute for the digital sciences | Grenoble, Rhône-Alpes | France | 8 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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will investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable
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counting, or similar areas. Additional experience in the theory and practice of machine learning would be an asset. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8188-MARHEC-007
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Your Job In this position, you will be an active part of our AI Consulting Team. Together with our partners, we develop new and innovative applications of Machine Learning. You will connect to
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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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in terms of detection accuracy and reliability Your Profile Ongoing masters degree studies in computer science, data science, engineering, machine-learning, or other closely related field Good