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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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research projects and also industry collaborations, involving the following topics and tasks: Integration of quantum communications in new mobile network infrastructures, 5G and 6G. Development of mechanisms
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. The research will involve both empirical and methodological contributions: Developing automated frameworks to detect and analyze cross-platform data flows using network measurements, static and dynamic app
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for the detection and analysis of vulnerabilities and privacy harmful behaviours in IoT products, including side-channels arising from misuse and abuse of network protocols. The candidate will develop and apply