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) before the human eye can see them. The principal aim of this PhD research program is to develop methods to improve the hyperspectral image classification using deep learning techniques. The developed
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deep learning theory, Bayesian statistics, and generative modelling, this work will advance our understanding of both the capabilities and vulnerabilities of modern AI systems. This will have potential
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interaction and human motion analysis Prior knowledge of machine learning/deep learning applied to motion analysis (e.g., relevant courses and research experience) would be an advantage IELTS score of 6.5
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group has implemented state-of-the-art deep learning for underwater communications; deep learning models underwater environment based on real data. Our preliminary study shows that state-of-the-art deep
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deep learning. The purpose of this scholarship is to support a PhD student to contribute to the advancement of infrastructure monitoring technologies with strong industry collaboration. Student type
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deep learning, imaging and data analysis would be helpful for this project. Must be eligible to enrol in PhD programs at Curtin University. Application process Please send your CV, academic transcripts
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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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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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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have