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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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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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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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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to adel.bibi@eng.ox.ac.uk For more information about working at
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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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are looking 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
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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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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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About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral