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, sociological...) to measuring beliefs, attitudes, and their relationship with shaping (online) behavior is desirable. Familiarity with data analysis in R or Python is required. Experience in study handling
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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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cooperation with Kopter Germany GmbH and the Engineering Risk Analysis Group of Prof. Straub, which provides information on both the health and the actual stress of helicopter components. For this so-called
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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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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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• Experimental investigations using atomic force microscopy (AFM), including force-distance curve-based imaging, (single molecule) force spectroscopy, nanomechanics, and associated data analysis • Application
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on Responsible Data Science. The PhD positions will be at the intersection of Data Science and Social Sciences and will focus on topics such as Explainable & Fair AI, AI Auditing, AI Alignment, and AI Safety in
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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