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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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analysis, modeling and applied ML experience is recommended, given the need to identify patterns in large-scale behavioral logs. Software reverse engineering (Frida, IDA Pro) experience is recommended
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