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the perspective towards generating data for entrepreneurial seed funding (GO-Bio etc.) for a further development of the technology towards the market. In that sense we also highly encourage candidates that would be
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efficient and safe trajectory planning in safety-critical scenarios. For this purpose, we focus on modeling and quantifying risks in order to subsequently incorporate them into trajectory planning. The goal
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and simulation tools. Research topics include geometric modeling of engineering products, methods of geometric analysis, methods of Building Information Modeling, modeling and simulation of construction
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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robotic sample retrieval system that will be able to obtain surface swabbing samples of critical infrastructure buildings for chemical or biological analysis. The cohort of PhD students are spread around
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the objectives more precise; working on the individual PhD study project with its focus on the methodological contributions as well as on empirical data processing for the case study analysis in collaboration with
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distribution analysis with collaborators at the University of Ljubljana in Slovenia. Data analysis and manuscript preparation. Presenting results at international conferences. Training Master’s and Bachelor's
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its rich information content, conventional analysis methods have not yet fully realized its potential. This research project aims to develop a robust AI foundation model based on modern Transformer
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-situ measurement network and perform terrestrial laser scanning, analyzing microclimate data and their relation to forest structure, and using optical satellite time series and radiative transfer models