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management to improve the performance of the future wireless communication systems. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we
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The PhD candidate will generate and study complex macroscopic flow patterns of LCE precursors, polymerize the precursors into LCEs programmed by the flow patterns, and study the resulting actuation
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processing, signal processing, and network resource management to enhance performance. To optimize and analyze complex 6G networks, we use AI/ML, graph theory, and optimization techniques Furthermore, our
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the performance of the future wireless communication systems. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as
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, production-grade machine learning solutions for predictive modelling and complex decision-support systems. Develop scalable and efficient ML pipelines using MLOps best practices. Address challenges related