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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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Network encryptor application. Targeted applications include agile high throughput network encryptors, code-signing engines, or key management modules. The outcomes of the project will be applied in novel
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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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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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outcome is the design of methods, techniques and their prototype to implement trusted PQC migration. The potential benefit is to enhance the security of Australian critical infrastructures against quantum
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autism [4]. For example, to measure anxiety, researchers may place a rodent in a large tub, record a top-down video and measure the time spent near the safety of walls [2]. These videos also contain rich
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methods, the goal is to enhance the ability to identify and mitigate the risks posed by fraudulent online platforms. Required knowledge Python programming Machine learning background Text analysis Image
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the work had largely relied on code written by a young open source developer, who did not receive any renumeration from the sale (and who in turn, relied on code and ideas developed by AI researchers and
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, which includes images and structured data. Learn more about minimum entry requirements . Primary supervisor Teresa Wang Apply now Supervisor Connect Supervisor Connect allows you to explore cutting-edge
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Diseases team and contribute to vital surveillance and data linkage work on respiratory viruses, including influenza and COVID. The position is responsible for managing data from sentinel systems such as