387 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" research jobs in France
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details of 2-3 references to laura.cantini@pasteur.fr For more information : https://research.pasteur.fr/en/team/machine-learning-for-integrative-genomics/
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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/13 04:59 AM UnitedKingdomTime** (posted 2025/10/21 05:00 AM UnitedKingdomTime, listed until 2026/04/22 04:59 AM
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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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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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(graduated or close to graduation) in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields. Scientific curiosity and creative thinking
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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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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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ICT Services & Applications. Your role This position sits at the interface of machine learning, uncertainty quantification and computational biomechanics. You will work within the Legato group and in
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material