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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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We are seeking a creative and highly motivated postdoctoral researcher to join the Turing AI World-Leading Fellowship research programme led by Professor Alison Noble. This exciting and ambitious
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at the intersection of these research areas. You should hold, or be close to completing, a PhD/DPhil in mathematics, statistics, physics, engineering, data science, or a related field. Experience in cancer
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base, the partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will
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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics
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projects in computer vision research, with a particular emphasis on Spatial Intelligence, 3D Computer Vision, and 3D Generative AI. You should hold a relevant PhD/DPhil (or near completion*) in Computer
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The Kelly lab welcomes individuals with diverse career backgrounds – PhD-level scientists in any discipline with expertise in data and programming, or software engineers outside of academia looking to change
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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to implantation, including validating methods in virtual in vivo environments. • Collaborate with engineers to refine fuel cell specifications and support in vivo experiments. You should hold a relevant PhD
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of Engineering Science (central Oxford). The post is funded by the EPSRC research funds and is fixed-term for up to one year. The successful applicant will have experience on using sequential decision-making methods