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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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hybrid AI (combining machine learning, feature-based modelling, and classical OR); Designing intelligent release, workload control and material planning methods that stabilize flow, improve on-time
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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: Develop real-time optimization and hybrid AI models for end-to-end multimodal transport planning under uncertainty. Design synchronization, consolidation, and matchmaking algorithms that align prefab