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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where
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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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. 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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the area of end-to-end modular autonomous driving using computer vison and deep learning methods. This includes developing an efficient and interpretable image processing, vision-based perception and
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neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis
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]). This project investigates methods to enable high performing NMT in low-resource scenarios. [1] Neural Machine Translation for Bilingually Scarce Scenarios: A Deep Multi-task Learning Approach Poorya Zaremoodi
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this role, you will have a degree in finance or accounting, CPA or CA status, or equivalent experience in complex financial environments. You will bring deep expertise in statutory reporting, Australian
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improvement across the University's financial ecosystem. To be successful in this role, you will have postgraduate qualifications in accounting or business with current CA/CPA accreditation, alongside deep