The position addresses the fundamental question of how learning and inference should be embedded inside future networked systems. The researcher will develop novel paradigms for distributed, federated, and continual learning tailored to highly dynamic, resource-constrained, and privacy-sensitive environments. The work aims to tightly integrate learning processes with network and system behavior. o Design and evaluate distributed learning algorithms that operate across heterogeneous nodes, exploring model splitting, collaborative inference, and adaptive training strategies. o Investigate privacy-preserving and energy-efficient learning mechanisms and study the co-design of AI models and network architectures - Event Organization: Support the organization of online webinars, events, workshops, and conferences related to the Chair's activities.
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