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project is to build upon recent research from our teams by developing innovative machine learning models for these graphs, which are capable of (i) discerning specific traits of individuals, such as gender
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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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compact laser-plasma injector to inject a small ring and pioneer in application of machine learning particle accelerator. Key reasearch topic associated to the pHD project: 1. Data-Driven Optimization
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to teach in English or French. Practical details PhD will take place at IRIT, the largest computer science research institute in Toulouse, Our team SEPIA works on resource management on various distributed
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is an impossible task in metasurfaces composed of millions of elements. The PhD aims to develop methods using machine learning surrogate models to solve this complex design problem. The project aims
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that optimizes the machine learning process, making it more accessible and efficient for users with varying levels of expertise [1]. AutoML leverages algorithms and computational capabilities to automate key
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will provide international collaboration with microbiologists and protein design groups with the possibility of in-site internships for 1-3 months. The PhD candidate will be hosted in the CAPSID team
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in computer vision, mainly for image synthesis [7, 8] and medical imaging [9]. The proposed PhD research program aims to investigate these recent advances in generative models with deep learning
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objective will be to develop algorithms for predicting and planning the evolution of local energy systems (microgrids) over a time horizon of several years, using machine learning and numerical optimization
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machine learning, speech science, cognition and behavioural studies. If all disciplines are addressed in this PhD position, candidates without expertise in some of the areas listed below are nevertheless