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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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. 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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to improve care, clinical management and clinical outcomes, and quality of life of people with spinal cord injury). How to apply Current Students For current QUT MPhil or PhD students please submit your
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adoption of lithium-ion batteries (LIB) in electric vehicles and energy storage globally is generating a significant volume of waste end-of-life lithium-ion batteries. The battery separator, 5
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commercial lithium-ion batteries do not satisfy the increasing demands of portable electronic devices and electric vehicles, due to low energy densities, safety issues and high cost. High capacity electrode
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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for QUT's Doctor of Philosophy , including any English language requirements. Enrol as a full-time, internal student. Have a background in electrical, mechatronic, or biomedical engineering, expertise in
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. from government, other funding body or current employer) not have secured an internally funded scholarship (e.g. scholarship from supervisor, school, or faculty) unless it has been explicitly identified
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burn survivor communities (PhD scholarship) Future and current student scholarship Work with a team of leading researchers to undertake your own innovative research in and across the field. Home Study
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current digital performance and co-design exemplars for practice, policy settings and scholarship. Through a qualitative, multi-method approach the research will generate resources for industry, case