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. The new system to be developed will be tested and optimized with an existing CargoKite ship prototype in real operation. Previous Work https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10530091 https
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predictive simulations, real-time control, and process optimization. You will work on the development of deterministic population balance models, conduct single-crystal and batch crystallization experiments
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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing
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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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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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Researchers: Ph.D. in Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent
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), and joint optimization—impact the model's accuracy, robustness, and generalization capabilities? Can fine-tuning a pretrained model like CLIP on domain-specific data (e.g., automotive or travel-related
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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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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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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