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regimes using simulation modeling Quantification of climate change impacts on protective forests (scenario analysis) Contributions to evidence-based management decision making in mountain forests
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analytical skills for model formulation and optimization Demonstrated research potential, ideally with a track record of publications in relevant venues (journals such as IEEE T-ITS, INFORMS Transportation
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at the Technical University of Munich (LeibnizLSB@TUM), a legal foundation under civil law based in Freising, is a prominent member of the Leibniz Association. Our institute integrates cutting-edge
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to characterize network-wide interventions, like reallocating road space or travel demand management schemes. Second, to develop an MFD-based traffic simulation linked to the agent-based simulation environment
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the pyramids and creating digital object models with numerical simulations, for example, using Salvus software or similar. - Publication of research results and presentation of results at scientific conferences
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML
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, and cohort-based human studies to explore how pollutant exposure influences metabolism, food preferences, and overall health. The successful candidate will focus on the biophysical analysis of receptor
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impact and adaptation analyses, and for spatio-temporal modelling as well as upscaling of ecosystem properties via remote sensing Interactive collaboration and exchange within the TUM Center for Forest
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optimization or discrete algorithms. Profound mathematical modeling and programming skills. Experience with the design and analysis of graph algorithms or multiobjective optimization models is a plus. Very good
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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