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water. Clearly identified scientific objectives motivate and guide the design and development of space mission by CESBIO and monitoring tools. Missions: - Data processing, apply and tune deep-learning
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under the guidance of Prof. Ivan Nourdin. Your role Conduct research in machine learning, deep learning, and probabilistic modeling, with a focus on real-world applications Disseminate research findings
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Deep Learning-type methods. The focus will be on geodesic methods, the search for paths of minimum length according to an adapted metric, imposing for example a penalization of the curvature. In addition
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FieldComputer scienceYears of Research ExperienceNone Research FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science
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This position will consist of using "deep learning" methods, in particular CNNs and "transformers" for the processing of data from the IASI instrument from CNES. These observations are brightness temperature
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integration (including environmental sensors and eye-tracking technologies), strong machine learning and deep learning skills (especially embedding models and spatial data analysis), and experience in
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urban walkability perception through hybrid sensing and Deep Learning -Macroelements in Earthquake Engineering -Experimental characterization of the sealing properties of caprock formation for CO2
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of Research Experience1 - 4 Additional Information Eligibility criteria - PhD in Phonetics/Phonology, Computational Linguistics, Automatic Speech Processing/Machine Learning or relevant related fields
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, having a wide range of applications, from astronomical imaging to computational photography. In recent years, (deep) learning-based solutions have obtained state-of-the-art performance in many applications
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computer vision. The dominant approach is based on deep neural networks applied to RGB images. These models have disadvantages such as: a) the need of large quantities of annotated data, which requires