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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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profile PhD in Mathematics, Theoretical Computer Science, Information Theory, Physics or related fields High level of mathematical maturity Experience with topics related to quantum LDPC codes and decoding
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, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and supported by the COMMLab , the 6GSPACE Lab , the HybridNetLab
Searches related to coding theory
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