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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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many real-world applications, finding a solution requires high-performance computer clusters that consume large amounts of energy and run for a long time. Our project aims to create a radically new
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights
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implementing signal processing algorithms specifically tailored to analyze signals that contain interfering impulsive content, often encountered in data coming from main and pitch bearings. Machine learning
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“Reinventing Multiterminal Coding for Intelligent Machines (IONIAN).” Why cooperative perception? State-of-the-art autonomous vehicles can miss a critical obstacle when their camera is blinded by sun glare
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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cells, embryo models and human embryos, with a focus on understanding how cell stress impacts these processes. During your PhD, you will work very closely with a postdoctoral fellow that will be carrying
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and concerns about the dominance of the highly educated. Against that background, this PhD project aims to achieve a better understanding of the particularities of education-based status processes by
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for the future design of urban transformation processes, ensuring that best practices can inform future policy and planning. The expected results are: 1) Critical analysis of the impact of mobility campaigns