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how oxygen, nutrients, pollutants, or fine sediments migrate between surface water and the bed, or to quantify how restoration actions affect life-sustaining processes such as fish spawning and methane
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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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the MATSim agent-based transport simulation framework. The main task is to enable simulated agents to choose transportation modes, such as car, bus, bike, or walking, based on real-time feedback from
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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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during downstream processing. Theoretical investigation of molecule stability during DSP, using molecular interaction simulations such as e.g. Molecular dynamics (MD). Combination of PAT and data driven
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, novel 2D and 3D processing techniques, and integration into devices. Various synthesis, manufacturing, and experimental techniques will be utilized and coupled to cutting edge simulations, facilitating
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simulation with flotation Presentation of own results at international conferences, in peer-reviewed journal publications and in a PhD thesis Cosupervision of related MSc topics and cooperation with other
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: Prof. Dr. Steven Travis Waller, Chair of Transport modeling and simulation, and co-supervised by at least one additional professor, plus an international tutor of the CRC Requirements: excellent
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree