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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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to co-develop tools that are embedded into routine health systems and data dashboards used to guide policy decisions. This role sits at the intersection of generative machine learning, statistical
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for the challenging tasks facing biomedicine and public health. We are seeking a highly motivated Research Associate in Biomedical Machine Learning, who is passionate about advancing machine learning methodologies
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and enabling commercial deployment. The work will integrate model based design of experiments, machine learning, hybrid and kinetic modelling (digital twin development), process design, simulation and
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rigorous, collaborative research aligned with project goals. Develop and apply deep learning models, particularly in computer vision, NLP, and multimodal systems. Publish in peer-reviewed journals and
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM
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machine learning and hardware assurance. The project’s interdisciplinary nature provides a strong foundation for careers in applied research, secure electronics, or further academic study, with real impact
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on a new project called TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring
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the delivery of the school’s learning and teaching outcomes, including working with conventional machine tools/hand tools within an Engineering Machine Shop environment. Ability to use good
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have forged a formidable history. As a teaching-intensive University, our courses are rooted in practice-based learning, and we provide wider support that sets our students up to succeed after graduation