33 machine-learning "https:" "https:" "https:" "https:" "https:" "CNRS " uni jobs in France
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- Institut Pasteur
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- Ecole Pratique des Hautes Etudes - PSL
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to the development of state-of-the-art AI approaches applied to land surface monitoring, particularly using satellite observations. These approaches may include machine learning and deep learning methods
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to structured programming in C++ and Python - knowledge of linux / unix operating system - fluent knowledge of spoken and written English - fundamental knowlegde of machine learning (and statistics) - good level
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at the École Pratique des Hautes Études – Université PSL. AOROC is a laboratory across the École Normale Supérieur (ENS), the EPHE and the French national centre for scientific research (CNRS). It comprises
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% with Laura Cantini’s team and 20% with the Bioinformatics and Biostatistics HUB. Information about the teams : The Machine Learning for Integrative Genomics team : https://research.pasteur.fr/en/team
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, and/or multiphysics modelling • Mathematics & AI: Numerical analysis, inverse problems, neural networks, scientific machine learning • Programming: Python (scientific computing, ML), preferably C
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. The PhD will focus on two complementary approaches: 1) Enhancing CDI with machine learning: improve this technique using convolutional neural networks (CNNs) trained on simulated data, enabling faster and
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, artificial intelligence—and particularly machine learning methods—has become indispensable. Depending on the specialty, processed data may include numerical values, point clouds, text or images, often
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project led by the company BUAWEI, located in Lille (59), the Laboratory of Mathematics of INSA Rouen Normandie (UR 3226, FR CNRS 3335) is recruiting a Research Engineers (IR). Expected work: Real-time
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with machine learning approaches Knowledge of muscle mechanics (Hill muscle model or similar) Previous work on simulated bodies or animal locomotion Your Role You will work collaboratively with a
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processing, neuromorphic engineering, or a closely related field. A solid background in machine learning is expected, with interest or experience in spiking neural networks, temporal modeling, or bio-inspired