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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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research in the broad field of computational methods for the built environment. Particular emphasis is placed on building information modelling, digital drawings, point cloud capturing and processing as
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. The group’s work in this area has led to best paper awards at PacificVis and Graph Drawing, with recent publications in IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum
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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 testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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or for multiobjective optimization problems. Implement the developed algorithms (e.g., in Python) and evaluate their practical performance on artificial and/or real-world data. Teach tutorials (in English) for
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Research Center (CRC) “Data-driven agile planning for responsible mobility” (AgiMo), funded by the German Research Foundation (DFG). This interdisciplinary center, involving four universities and the German
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Course language English and German Current information Application deadlines: 15 March and 15 September Financial support No Structured research and supervision Yes Research training / discussion Yes