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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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; wireless sensor networks; green communications; next generation wireless broadband networks; signal processing and acoustics; digital image processing; structural health monitoring and underwater
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. on Artificial Intelligence, UNE, Armidale, Australia, November 1994, pp37-44 Wallace, C.S. and D.L. Dowe (1999a). Minimum Message Length and Kolmogorov Complexity, Computer Journal (special issue on Kolmogorov
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established protocols to ensure consistent, high-quality imaging outcomes. Ensure efficient processing, organisation, and secure storage of research imaging data in accordance with Facility and University
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of learners as they develop. 3. Develop and evaluate a presentation paradigm to enable non-data science savvy users to get actionable insights into the findings obtained through the measurement framework
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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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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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those utilising computer programs to conduct examinations set up of examination venues, including paperwork and computer equipment, receiving, and checking computer systems to facilitate the efficient
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into practical, scalable AWS solutions. You'll prototype, build, teach, and collaborate — working across RAIL, RACE, ITS, and AWS to make cloud technology genuinely accessible and impactful for RMIT researchers