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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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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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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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. The role will also provide the opportunity to learn and develop skills in structure-based drug design. The role is ideal for someone who has, or is about to be awarded, a PhD in structural biology and would
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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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for secondments in one of the collaborating institutions and/or to co-supervise a PhD student. A secondary affiliation to EPFL or UoE may be offered to candidates with the appropriate profile. Appointment is
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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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. For this multidisciplinary project, we are looking for a highly motivated cell biologist who holds a PhD in a relevant field with a commensurate publication record. We welcome applications from candidates with a background in
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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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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