432 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Postdoctoral positions in France
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of the project. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7587-PEDBAR-007/Candidater.aspx Requirements Research FieldEngineeringEducation LevelPhD or equivalent Research FieldPhysicsEducation
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Website https://emploi.cnrs.fr/Candidat/Offre/UMR9002-ANNRAM-002/Candidater.aspx Requirements Research FieldBiological sciencesEducation LevelPhD or equivalent Research FieldBiological sciencesEducation
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. To that aim, both Stein-based bilevel optimization, empirical Bayesian and unsupervised deep learning approaches will be considered. The recruited postdoc researcher will tackle both implementation challenges
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de réacteurs auto-agité par jets gazeux. Le candidat embauché aura l'opportunité de rejoindre un groupe multidisciplinaire composé de 6 doctorants, 4 postdoctorants et 3 chercheurs académiques
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integrated more specifically into the molecular modeling department of the ICCF. To learn more about the staff, techniques, and expertise of the modeling service, applicants can visit the ICCF modeling service
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ExperienceNone Additional Information Eligibility criteria - Holding a doctoral degree in particle physics - Experience in C++ and Python programming is desired - Experience in training and using Machine Learning
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to €15 per month, subject to conditions - staff restaurant at a preferential rate - car park - leisure, sports and cultural activities via the social action and mutual aid committee (CAES du CNRS) or local
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of Python programming and a deep learning framework, preferably PyTorch. Solid knowledge of image processing, inverse problems, and machine learning. Significant research experience, demonstrated by quality
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through MathJobs.Org right now. Please apply at https://applications.sciencesmaths-paris.fr/en/2012-2013-postdoctoral-positions-380.htm . Contact: Kevin Ledocq Email: Postal Mail: Fondation Sciences
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reliability. · Understanding of hardware accelerators for AI and their operation. · Familiarity with machine learning workloads (e.g., CNNs). As this is a research position, it is necessary