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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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– 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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will lie on developing machine learning models for regression and reinforcement tasks to work with, enhance or replace established methods from computational engineering and computer simulation (such as
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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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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by the German Research Foundation (DFG), at the University
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains