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Postdoctoral Research Associate in Deep Learning Theory to work on approximation theory of deconvolution, convolutional neural networks, operator learning, distribution regression, and graph neural networks
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2-year, Full time fixed term position. Located at the School of Mathematics and Statistics, Camperdown Campus Opportunity to contribute to research for Deep Learning Theory at the University
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Closing date: 23 June 2024 Campus: School of Mathematics and Statistics at the Camperdown Campus, The University of Sydney Position: Postdoctoral Research Associate in Deep Learning Theory
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insufficient. Recently our research group has developed deep neural networks to enhance underwater communications. Our preliminary study shows that deep learning models are able to improve accuracies
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biodiversity and sustainability research. The initial objective is to use deep learning techniques to perform acoustic species identification in real-time on low-cost sensing devices coupled to cloud-based
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Although deep learning has produces state of the art results on many problems, it is a data hungry technology requiring a lot of human supervision in the form of annotated data. Potential PhD topic
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Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic
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process by leveraging deep learning including automated app functionality summarization [1], UI design generation [2], and front-end code generation [3]. We hope to explore more in this direction including
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propose a model to proactively locate accessibility issues and recommend potential fixes to their app based on deep learning models. On the other hand, we will also propose a new approach based on
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on leveraging advanced computer vision (CV) and deep learning (DL) techniques to develop a state-of-the-art computer-aided detection and diagnosis (CAD) system for early-stage Alzheimer's disease (AD) detection