41 machine-learning-"https:"-"https:"-"https:" Postdoctoral positions at University of Oxford
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We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is fixed-term for 1 year
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for flexibility in the start and end dates, subject to approval by the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research
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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 [email protected] For more information about working at
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About the role Applications are invited for a Postdoctoral Research Associate in Artificial Molecular Machines to work under the supervision of Professor Matthew Langton for a period of up to 24
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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting- edge deep generative probabilistic models
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/DPhil in robotics, computer science, machine learning, informatics, AI, or a closely related field. You will have an excellent academic track record in topics relevant to locomotion and manipulation; path
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public health. In this role, you will develop and evaluate novel AI and machine learning methods using large-scale multimodal datasets, contributing to epidemiology-informed foundation models, predictive
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to have published in leading machine learning conferences or similar venues. One or two PDRAs will be recruited to work within one of, or across, the four research themes: Learning with Structured
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Weather and climate prediction are undergoing a profound transformation. Alongside traditional physics-based forecast systems, machine-learned (ML) weather prediction models, hybrid ML-physics
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machine learning approaches to investigate the mechanisms underlying GC-biased gene conversion and understand how meiotic recombination shapes human genetic variation and genome evolution. Working closely