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factors involved in the onset and progression of dementia. Advanced computational methods, including bioinformatics pipelines and machine learning, will be employed to uncover putative biomarkers and
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. Through choreographed interactions with movement experts, this project expects to generate machine learning strategies to understand how people and robots can reliably and fluently move together. Expected
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student
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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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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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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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interactions. Machine learning: reinforcement learning, or multi-agent systems. Signal processing: spectrum sensing, localization, or radio environment modelling. Multi-agent systems: distributed intelligence
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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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) 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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. The underwater acoustic communication technologies will help. The school is focusing on research in AI/machine learning and signal processing which are the research areas in this proposed project. We have