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/Qualifications Solid academic background in one of the thesis fields (e.g. computer science). Experience(s) in federated learning, privacy-preserving machine learning or cybersecurity is preferred. Competence in
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algorithms e.g. using machine learning approaches for enhanced data analysis and acquisition, experiment automation, etc. The work will take place in the Algorithms & scientific Data Analysis group, in close
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analysis during experiments. You will also explore new avenues in analysis using novel algorithms e.g. using machine learning approaches for enhanced data analysis and acquisition, experiment automation, etc
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computing problems have been explored from the perspective of machine learning and Artificial Intelligence (AI). The combination of AI with computational sciences has given rise to a wide spectrum of
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approaches and reduced-order models. It will lead to the development of efficient machine learning models that will be used in a global optimization framework. The first step is to determine the most
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in Entreprise Blockchain and Artificial Intelligence Trustworthy artificial intelligence (AI) is a multifaceted concept underpinned by three critical pillars: Trustworthy Machine Learning, Trustworthy
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managed through power reserves that are provided by synchronous machines. The significant penetration of DERs, connected to networks by power electronics, tends to reduce the number of synchronous machines
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machine learning. The choice of the most suitable machine learning technique for such modeling depends on the quantity of data, its quality and the time available to build the model. Hybrid modelling
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Learning for Quantum" project Machine Learning for Quantum is a coordinated doctoral training network to explore how ideas and techniques from machine learning and quantum can feed into and benefit the other
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, with the objective of maximising online data analysis during experiments. You will also explore new avenues in analysis using novel algorithms e.g. using machine learning approaches for enhanced data