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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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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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. This is indeed what AIC, BIC, MDL and MML would anticipate. And yet deep learning methods can often work despite this. This project investigates how deep learning can survive over-fitting and whether
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focused on its purpose of changing lives through revolutionary learning and research with real impact. Our game changing Southern Cross Model brings a deeper learning experience, improving individual
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dual-sector learning and research by 2028. Join us on the journey and help us achieve our strategic drives embedded in our Strategic Plan 2022-2028: Start well, finish brilliantly. One of the drivers
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for research leadership. Strong experience in at least two of the following areas: reinforcement learning machine learning, deep learning dynamic scheduling, optimisation and control Experience in developing