Fostering Scientific Exchange
By combining expert lectures, doctoral student presentations, and discussion sessions, the summer school aims to encourage scientific exchange among participants and to provide young researchers with the theoretical foundations and practical insights needed to design robust, distributed, and efficient AI methods.
Topics covered include distributed AI at the network edge, distributed inference, AI for radio access networks, Bayesian optimization for telecommunications, robust machine learning, and reinforcement learning.
AI in Industry
The speakers, many of whom come from industry (including Ericsson, Orange, NVIDIA, and Hivenet), will present the state of the art in artificial intelligence applications within their respective sectors.
Topics will include technology, regulation, and economics in the telecommunications networking industry; challenges related to energy availability; digital sovereignty; and inference optimization in resource-constrained environments. These are all subjects that may be of particular interest to early-career researchers considering a career in industry.