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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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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
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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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partnership with the German Federal Waterways Engineering and Research Institute (BAW); building a fully coupled model that simulates surface hydraulics and subsurface flow with relevant turbulence models and
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at the nanometer scale. We will use available DNA thermodynamic database, coarse-grained simulations of DNA motifs, and existing experimental data to establish an AI model that is able to guide the construction
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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resource-efficiency requirements. This collaborative doctoral project brings together the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy
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. The BEAM projects cover a wide spread of topics, including theoretical physics and chemistry work. For example, our targeted syntheses are supported by models of self-assembly for specific types of molecules
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and the effects of disordered correlated microstructures on diffusion; iii) development of energy-based models and numerical simulations of hyperuniform assemblies; iv) development and application
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resource-efficiency requirements. This collaborative doctoral project brings together the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy