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16.07.2025, Wissenschaftliches Personal A PhD position (75% TV-L E13, to be extended to 100% after 8 months) in discrete optimization is available at the Professorship of Optimization and
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17.07.2025, Wissenschaftliches Personal The Chair of Software Engineering & AI is searching for a new doctoral research associate (TVL-E13) in Generative Engine Optimization. Research focus
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data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning, and artificial intelligence with
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Iterative Algorithms: Optimization and Control.” About the Project The focus of the project is the analysis of iterative algorithms arising from time discretizations of nonlinear evolutions of various kinds
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity
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purely motor-powered ships and will be marketable as an independent product. The new system to be developed will be tested and optimized with an existing CargoKite ship prototype in real operation
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of stochastic modelling, optimization, and data analytics. The position is available from January 2026 and is fixed for a term of 3 years, with the possibility of extension. As part of this role, candidates will
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
geometry, algorithmic complexity, and combinatorial optimization. Our contributions are regularly published in top venues like the Conference on Artificial Intelligence (AAAI), the Symposium on Algorithms
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Munich GmbH we started a joint project to build optimization and simulation models for the operations of Terminal 2 of Munich Airport. The joint target is to derive tactical and strategic evolutions