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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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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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The project involves building and curating a comprehensive food image dataset suitable for mobile AI applications. High-accuracy deep learning models will be trained on this dataset and then
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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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disease patients using radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical
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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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radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical imaging. Within our research
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electrochemiluminescence (ECL). Proven experience developing prototype sensors, including surface modification and electrochemical interface design. Capability to integrate optical characterisation methods (e.g
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, ensuring research goals are met create, manage and populate a database of primary research documents coordinate data management processes, ensuring thorough analysis and reporting streamline administrative
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photoelectrochemical ammonia synthesis funded by the Australian Research Council The successful candidate should have research experience in electrochemical technology and prototype design. They will have excellent oral