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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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level of independence, project outcomes and impact, and references in terms of publications, theses, technical reports, simulations, analysis, code samples, or documentation (multiple references
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, and references in terms of publications, theses, technical reports, simulations, analysis, code samples, or documentation (multiple references are considered beneficial). An evidence-based CV
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of publications, theses, technical reports, simulations, analysis, code samples, or documentation (multiple references are considered beneficial). An evidence-based CV that reflects a representative array of
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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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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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complex materials with tailored properties using high-throughput simulations, data analytics, and material characterization. Benefit from strong connections to top research infrastructures like the Jülich
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simulation. Description of the PhD topic (subproject B7 - New methods for multimodal microscopic simulation of street users’ behavior in shared space contexts): This PhD project will develop and implement
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management (RDM) system. The goal is to create an overarching data space for the RTG that integrates various experimental techniques and simulation methods. This includes concepts for ensuring data quality
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approaches but also to practically test them directly on real test vehicles – from first simulation to real-world driving. An innovative working environment with modern laboratories and simulation environments