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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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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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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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of reconfigurable nonlinear processing units (RNPUs, [Nature 577, 341-345, 2020[(https://www.nature.com/articles/s41586-019-1901-0). In this PhD project, you will work on the development of efficient machine learning
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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
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1