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) Interpretable machine learning for network adaptation. In this thesis, the student will study how interpretable models and explainable learning algorithms could be used in real cellular networks for safe
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of federated algorithms that enable fair management of the advantages and duties of energy communities in which vulnerable people live. The automatic generation of data models using AI technology will enable
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implementation of security provisioning algorithm in DFL environment Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » Communication
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III of the Call for Proposals - Demonstrable experience in tasks related to multibody systems dynamics, particularly slack modelling and data fusion algorithms with computational models. - Candidates
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III of the Call for Proposals. - Demonstrable experience in tasks related to multibody systems dynamics, particularly slack modeling and data fusion algorithms with computational models. - Candidates
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, CU/DU/RU split). - AI/ML algorithms and methods for joint compute and radio resource management at the 6G edge leveraging accelerators - Control- and data-plane separation, real-time scheduling, and