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vision and machine learning methods for multimodal imaging and real-time analysis in colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection
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, multimodal imaging, and AI-assisted diagnostics to enable safer and more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning
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for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets
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(KCL, London, UK) but will also have the opportunity to travel and work at the Centre for AI and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU
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reactions. We welcome applicants from diverse backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate
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PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets Experience in statistical or scientific programming (ideally R and/or
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intelligence; can present at national and international conferences; and prepare manuscripts as first or joint author for submission to leading journals and conferences in machine learning, cancer research and
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Vision, Robotics, Evolutionary Computation, Deep Reinforcement Learning, and Machine Learning. This should include a proven publication track record. You should also have: Research Associate: A PhD (or
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and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU). Importantly, we adopt a flexible working environment within the lab and are happy
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(KCL, London, UK) but will also have the opportunity to travel and work at the Centre for AI and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU