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projects Designing for complex or high-consequence systems Human-centred product, service, or interaction design in healthcare, defence, or critical infrastructure Applied human-computer interaction or
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The successful applicant will conduct research to design and develop novel machine/deep learning based trust technologies for securing IoT services/devices. The successful applicant will conduct
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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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Building tools to detect or prevent unsafe AI outputs Exploring regulatory gaps and proposing solutions This is an ideal opportunity for candidates with interests in machine learning, public health, ethics
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models like SWMM are computationally slow and lack scalability, while opaque AI methods risk biased outcomes. This project addresses these gaps by developing a responsible machine-learning framework
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structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data analysis techniques, are preferred. Application process To apply
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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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degree with strong skills in programming and machine learning. Please contact Zhuang Li for more information. The project focuses on developing multilingual datasets and advanced methods to detect and
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that combine fairness, privacy and legal guarantees for ADM systems, such as recommender and machine learning based systems. It takes a multi-disciplinary approach and although focused on the mobilities and
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technologies, social structures, and networks. Effective C2 organisational systems are critical not only to military settings, but also to the operation of many civil domains, including emergency response