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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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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 of desired secondary structures
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physics, microbial ecology, plant nutrition, plant physiology, plant ecology, biochemistry, and/or bioinformatics Strong interest in using process-based mathematical modeling to simulate biogeochemical
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, natural hazards management or related fields Interested in protective forests and their management Good quantitative skills (e.g., data analysis, simulation modelling, remote sensing) Good communication
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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to facilitate a rapid and efficient exchange among experimental and computational groups and Devise an approach in invertible predictive modelling that links semiconductor properties to the composition of lead
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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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measurements in a team of experts on and in the pyramids and creating digital object models with numerical simulations, for example, using Salvus software or similar. Publication of research results and
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of bespoke probabilistic models and/or evolutionary simulations, robust knowledge of and an affinity towards mathematical, computational or probabilistic modeling are important. Further skills in modeling and
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-22 eV or better, and powerfully test the Standard Model of particle physics. They further constrain CP-violating new physics at scales of 10-100 TeV, far beyond the reach of the LHC. The TUM and the