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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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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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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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affect surface outcomes, benchmark against conventional techniques, and evaluate performance of the finished components. You’ll also delve into intelligent automation and machine learning to optimise
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. Specific projects seeking applications are: Accelerating the discovery of inorganic solar-cell materials via a closed-loop, fully robotic synthesis–characterisation platform driven by multi-agent machine
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and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention
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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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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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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