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: - Apply unsupervised machine learning concepts to the analysis of continuous seismograms recorded in the vicinity of active volcanoes, in order to extract information about the state of the volcano and the
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, 2024. The field of embedded computer vision has become increasingly important in recent years as the demand for low-latency and energy-efficient vision systems has grown. One of the key challenges in
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professors, teacher-researchers, engineers, and doctoral students. The team is mainly involved in the PALLAS laser-plasma accelerator project. Optimizing Laser-Plasma Accelerators through Machine Learning The
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280 engineers and technicians) in all major areas required to design, develop, and implement experimental devices necessary for its scientific activity: mechanical, electronic, computer science
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specialized in Technology-Enhanced Learning (TEL) and Human-Computer Interaction (HCI). In particular, SICAL has extensive experience in behavior analysis using multimodal data in different contexts, including
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communications that are viable, efficient and compatible with physical and human reality. Our work is based on mathematical and computer theories for the development of models and algorithms, validated by hardware
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Laboratory, and the École Polytechnique, including office space, desktop computer, bibliographic databases, and computing clusters; Access to national high-performance computing centers. Dielectric properties
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, control, machine learning. Requirements Research FieldEngineeringEducation LevelPhD or equivalent Research FieldComputer scienceEducation LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD
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. Required skills include data analysis, proficiency in statistical and computer tools, climate modeling, risk modeling, and literature research. In terms of soft skills, autonomy, critical thinking
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in the field of medical imaging. The team particularly studies the potential of machine learning methods for an efficient and relevant representation of medical data such as images. The challenges