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deep learning. The purpose of this scholarship is to support a PhD student to contribute to the advancement of infrastructure monitoring technologies with strong industry collaboration. Student type
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | 8 days ago
, further exploring the carbon-related impact of polymer breakdown. The project will also incorporate artificial intelligence (AI) and machine learning to optimise degradation processes and data analysis
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for innovative solutions to improve worker well-being. The project proposes a novel, integrated framework leveraging virtual reality (VR), the internet of things (IoT), and machine learning (ML). Workers will
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data, energy yield data, condition monitoring system data, nacelle lidar data, maintenance data etc), and decommissioning. Despite the wind energy sector’s success in data collection, significant
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efforts to contribute to safer marine operations, we actively explore possibilities to utilize both numerical and machine learning methods to enhance the accuracy and resolution of metocean forecasts. About
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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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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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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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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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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