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models through specific activation functions. This project will be undertaken in collaboration with Dr Hemanth Saratchandran and Prof Simon Lucey of the Australian Institute for Machine Learning, and
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university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
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) use computer vision/machine learning to quantity athlete performance. Develop new computer vision/machine learning methods to enable measurement of sports performance. Research program would make use
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This scholarship aims to develop practical methods for optimisaton in large supply chain operations. Ideally candidates should have strong AI, machine learning, and optimisation backgrounds
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external enrolment procedures. Selection criteria Demonstrated experience in programming and system development. Expertise in Python programming and data analysis. Experience developing Machine Learning
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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