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The Combinatorial Optimization and Graph Algorithms group primarily focuses on research and teaching in the fields of discrete algorithms and combinatorial optimization. The group develops efficient algorithms for discrete optimization problems and analyzes their computational complexity. Their research interests include network flow problems, particularly time flows, unsplittable flows, scheduling models, and stochastic online scheduling. They explore variants of these problems from the perspective of algorithmic game theory. The work emphasizes applications in traffic, transportation, and logistics, often involving interdisciplinary collaborations with researchers and industry partners. Methodologically, the group is focused on efficient approximation algorithms with provable performance guarantees, the analysis of linear and higher-order relaxations, as well as robustness and sensitivity aspects of solutions to mathematical optimization problems. They also contribute to rigorous mathematical analyses of methods involving artificial intelligence, neural networks, and meta-heuristics.
Requirements are consistent for general engineering and computer science programs. Specific advanced master's (MBA/Energy) may require 1 year of professional experience.