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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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leading scientists from mathematics, particle physics, astrophysics, and cosmology at the University of Hamburg and DESY. As part of the cluster's technology transfer activities, this project aims
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models and analysis pipelines for real-time performance, enabling adaptive imaging and feedback control Design active-learning and retraining strategies to robustly generalize to new microbial communities
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are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random network coding