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tools. In this PhD position, you will investigate the use of logic-based methods to provide decision support for the process of designing electromechanical systems. Here, the focus is not on the system
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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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characteristic of damaged gears or bearings. Furthermore, the increasing decentralization of wind farms necessitates robust and efficient on-site data processing capabilities. This PhD project will address
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are effective mechanisms to activate and consolidate the transitions as well as the resulting socio-spatial effects. The project is recruiting 13 doctoral candidates to pursue post-graduate studies towards
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-making (e.g. during operation, but also during design) to prevent over-consumption of fatigue life while balancing optimal production. The monitoring of fatigue loads along the entire substructure is a
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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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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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of UHPC behavior. By monitoring damage processes at an early stage, material optimization can be achieved, and prompt maintenance actions can be taken to prevent compromised performance, ensuring safety and
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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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offshore wind farms are coming online with tighter fatigue designs and more aggressive control strategies, your research will seek to assist decision-making (e.g. during operation, but also during design) to