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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning, and theoretical analysis using Leslie-Ericksen
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, and therapy resistance mechanisms Ability to work independently and collaboratively within interdisciplinary teams Prior experience with network modeling or machine learning is a plus We offer
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: TRR408-A7 Investigators: Prof. Dr. Ostap Okhrin, Chair of Econometrics and Statistics esp. in the Transport Sector and co-supervised by Prof. Dr. Kai Nagel, Chair of Transportation System
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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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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming