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Field
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the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be
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computer vision and machine learning methods to interpret the photovoltaic (PV) solar farm's condition and perform various inspections and anomaly detection. The research will draw from state-of-art
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the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including
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Biological Dark Matter (BDM). A main idea of this project is to make use of the pattern matching abilities of the Tsetlin Machine in machine learning to be able to recognize signals in the BDM in
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techniques. This research proposes a novel framework that integrates Machine Learning (ML) for structural health monitoring (SHM) and design optimization of CFDST wind turbine towers. The study will focus
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Europe. In the Monitoring & AI department, you will be involved in the development and implementation of AI and machine learning (ML) tools for monitoring and operation of CO2 storage sites. Key
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. The underwater acoustic communication technologies will help. The school is focusing on research in AI/machine learning and signal processing which are the research areas in this proposed project. We have
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learning system that creates a profile of wear and tear of turbines given the environmental, meteorological and physical conditions they operate under. Such data can inform structural health monitoring
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
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the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify