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communication and demonstrated computer skills including the use of word processing, spreadsheets and database applications • Demonstrated ability to engage and build trust with diverse cohorts and DET
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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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. • Drive initiatives that improve data quality, data governance, and predictive analytics capabilities • Lead the development and execution of AI and machine learning projects. • Build strong
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implement machine learning algorithms for complex data analysis. Utilise advanced programming skills (Python, MATLAB, C) and image processing libraries to develop computational solutions. Publish research
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mechanical loading of such samples. The focus of the PhD project will be to use machine learning techniques to better understand the interplay between the crystal orientations and deformation patterns in a
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very high resolution, suitable for detecting photovoltaic modules and the cleanliness of solar panels. These images and other data can be processed by computer vision and machine learning methods
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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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the direction of A/Prof Claudia Szabo in the School of Computer and Mathematical Sciences at the University of Adelaide. The project is a collaboration with Defence Science and Technology Group, within the Combat
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small-scale and/or large-scale structural testing, including operation of servo-hydraulic dynamic test frames, knowledge of control software, and knowledge of the basic concepts of hydraulic systems. Have
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approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own