1,668 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions in France
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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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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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. The project sits at the intersection of machine learning, neuroscience, and behavior, with applications ranging from music cognition to speech-in-noise perception in audiology. You will work with naturalistic
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Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
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]. Au-delà de la turbulence, les systèmes actifs denses peuvent également présenter des oscillations spontanées auto-entretenues, comme déjà observées dans les foules humaines denses et les tissus
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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spoken and written is required. The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/ ) at Institut Pasteur, led by Laura
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Montpellier, Languedoc Roussillon | France | 3 months ago
with skills in statistics and modelling through machine learning (Agronomy joint research unit, MIA joint research unit, Wageningen University & Research); (ii) national and international laboratories
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validate adaptive mechanisms for LoRaWAN based on machine learning techniques, targeting improved reliability and energy efficiency in mobile scenarios. To achieve this, it is necessary to go beyond