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learning new techniques and finding answers to problems. You want to take advantage of the opportunity to do your PhD in two different countries and learn from different cultures and expertise. Where
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climatic conditions, using machine learning approaches based on isotopic data. SSIAs for δ13C, δ15N and δ34S in dentin collagen and δ66Zn in enamel to reconstruct the evolution of seasonal habitats and the
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strong interest in computer science (software development, machine learning techniques, etc.) is desirable. · Applicants must have a maximum of 3 years of research experience after the PhD. · Language
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the field of algorithm configuration and selection in a streaming fashion by investigating techniques that continuously optimize machine learning models as new data instances arrive [2]. A key focus will be
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, where innovative ideas and scientific advances are encouraged and valued. Federated learning (FL) is a promising paradigm that is gaining grip in the context of privacy-preserving machine learning
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Technologies du Langage) department. This team specializes in machine learning methods applied to language processing and has extensive experience and international recognition in speech technologies. Where
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of Research ExperienceNone Additional Information Eligibility criteria The applicant should have a PhD in astrophysics or an equivalent qualification (e.g., in signal processing and machine learning). Skills in
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(FL) is a promising paradigm that is gaining grip in the context of privacy-preserving machine learning for edge computing systems [1]. Thanks to FL, several data owners called clients (e.g
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organized in four poles: “Computer Mathematics”, “Data Analytics and Machine Learning”, “Efficient and Secure Communications”, “Modeling, Simulation and Learning” and “Proofs and Algorithms”. The Ph.D