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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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Vision and Edge Computing'. PhD candidates involved in this project will be trained in the emerging field of smart infrastructure, which is critical for Australian society in the coming decade
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to integrate next generation satellite radar (InSAR) monitoring for ground motion with Global Navigation Satellite Systems (GNSS) positioning devices. This will focus on test sites where the student will
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the challenges of high cost, low efficiency, and poor durability. This project aims to develop a novel type of electrolyser device, called the all-perovskite inorganic anion exchange membrane water electrolyser
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brilliant success in past decades. PCFCs encompassing proton conductors regarded as the next-generation fuel cell device for their lower operating temperatures. As distributed electricity generation devices
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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