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This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods
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will bring your: Postgraduate qualifications in librarianship, information management, or education. Extensive experience in outreach, engagement, and service delivery. Deep understanding of research and
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experience in at least one of the following areas: deep learning, active learning, deep reinforcement learning, and natural language processing.
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one or more of the following areas: complex quantum processes, quantum error corrections, tensor networks, optimisation and machine learning, and developing software infrastructure Some experience in
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units and w's represent the weights of the neural network. References: [1] Buser Say, Ga Wu, Yu Qing Zhou and Scott Sanner. Nonlinear hybrid planning with deep net learned transition models and mixed
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levels for manufacturing, routing delivery trucks for transport, scheduling power stations and electricity grids, to name just a few. In recent years, deep learning is showing startling ability
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the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency. This project aims to deliver a smart software vulnerability
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‘dynamic graphs’. Although recently many studies on extending deep learning approaches for graph data have emerged, there is still a research gap on extending deep learning approaches for identifying
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Project description: On behalf of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), we will establish the role of artificial intelligence (AI) deep learning to improve the prediction
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segmentation and classification; for example, segmenting tumour from the medical images, and then classify the grade of the tumour. We will use various Deep Learning techniques, such as CNN, and will experiment