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twin for multistage crystallization processes used in industrial sucrose production. The goal is to create suite of first-principle and reduced-order models that enable predictive simulations, realtime
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demands. To break this bottleneck and cut simulation time by orders of magnitude, you will design and implement surrogate models that learn the behavior of full‑physics codes using modern machine‑learning
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for the simulation of non-adiabatic exciton transfer dynamics in light-harvesting complexes. The research will use a combination of quantum and molecular dynamics simulations, electronic structure calculations, and
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. Correlating experimental, ab initio and multi-scale simulation as well as machine learning techniques is central to our mission: Development and application of advanced simulation techniques to explore and
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computational scheme which allows efficient and accurate computer simulations of biofabrication. Using this novel tool in collaboration with our experimental colleagues we will thrive to understand the physical
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research and scientific exchange. To that end, we closely interact with experimental laboratories. You will: Develop, simulate, and analyze biophysical models of infection spreading and the inflammatory
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approaches (e.g., agile methods, model-based development, CI/CD pipelines) in concrete research projects. Publication of research results at national and international conferences. Assistance in preparing
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evaluation of efficient training and reconstruction pipelines involving deep learning models Find required initial conditions for LWFA simulations which yield the reconstructed observed electron buch shapes
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and Data Science (MIDS) at the KU Eichstätt-Ingolstadt. The research group works at the intersection of analysis, modeling and simulation. The advertised position is partly funded by the German Research
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traffic flow models and simulations for pedestrians, cyclists, and drivers of motorized modes, particularly in a shared space context but also for interactions in a general traffic context with dedicated