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Field
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, meteorological and physical conditions they operate under. Such data can inform structural health monitoring for offshore wind turbines or help plan new offshore sites, via estimation of power yield in relation
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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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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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, C., Tcherniak, D. (2022). On Explicit and Implicit Procedures to Mitigate Environmental and Operational Variabilities in Data-Driven Structural Health Monitoring. In: Cury, A., Ribeiro, D., Ubertini
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the area of structural health monitoring of civil engineering structures on an Australian Research Council Future Fellowship project “Innovative Data Driven Techniques for Structural Condition Monitoring
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to a wide range of engineering problems, including real-time structural health monitoring, vibration analysis, and control design. The ideal candidate will have an outstanding engineering or related
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the area of structural health monitoring of civil engineering structures on an Australian Research Council Early Career Industry Fellowship project titled, 'Transforming Smart Bridge Monitoring by Computer
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structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data
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of Fame, and the International Program on Water Management in Agriculture. Key Responsibilities: Field and Lab Work: Lead the collection of soil and water samples, maintain monitoring equipment, and design
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International Drainage Hall of Fame, and the International Program on Water Management in Agriculture. Key Responsibilities: Field and Lab Work: Lead the collection of soil and water samples, maintain monitoring