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Analysis, Image Analysis, Machine Learning and Computer Vision, as well as Mathematical Statistics, Probability Theory, and Mathematics Education. The degree courses lead to bachelor, master, and PhD degrees
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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description
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to the application deadline. PhD in computer science, electrical engineering, biomedical engineering, or a related field. Experience in Python programming, natural language processing, and multimodal deep learning
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synchrotron infrastructure tools for ex-situ and in-situ experiments to acquire essential information regarding the microstructure and the physical mechanisms involved during thermomechanical loading
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experience with machine learning techniques in general and neural networks in particular will be highly beneficial.
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Fellow will devote most of their time to research. There is the possibility of participating in teaching (up to 20%). Requirements PhD degree in Computer Science, Automatic control, Machine learning
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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and/or application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Tasks The tasks include primarily leading and conducting research
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for an excellent young life science or computational researcher to become Group Leader. Fellowships are targeted towards applicants to start their first independent group within a few years of their PhD. We offer