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a digital format. Computer visions techniques have been recently developed for structural health monitoring of civil structures, including vibration displacement measurements, crack detection and
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an ARC Linkage Project focused on developing an autonomous system for detecting and quantifying structural damage in infrastructures (e.g., bridges, grain silos) using computer vision, digital twins, and
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children. Mechanistic modelling of disease transmission involves the use of computer code to represent the epidemic dynamics of infectious disease spread within the community. This allows modellers
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to estimate their age, but if resorption occurs, it would lead to underestimation and inadequate conservation efforts. Additionally, investigating the role of mononuclear clastic cells in resorption during
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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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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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qualifications in psychology, human factors, artificial intelligence, human computer interaction, or a discipline that could shed light on individual and team dynamics within the context of command and control
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experienced supervisors, each with over 20 years of expertise in machine learning and computer vision. These supervisors have strong track records of research excellence, with numerous publications in top-tier