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We are seeking a full-time Postdoctoral Research Assistant in Computer Vision to join the Visual Geometry Group (Central Oxford). The post is funded by ERC and is fixed-term for 1.5 years with a
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work on large-scale analysis of complex traits, including Bayesian machine learning and linear mixed model approaches for trait prediction and association in high-dimensional genomic datasets, as
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
With the human population estimated to reach 9.8 billion people by 2050, the looming nitrogen (N) crisis, stemming from the intensive use of fertilisers in agriculture, requires urgent global action
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computer programs to design experimental paradigms, analyse data and conduct advanced statistical analysis. Prior experience in running neuromodulation studies including TMS and TUS is essential. You will be
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experts to acquire bespoke training and testing data; develop prototype solutions informed by the latest ideas in medical imaging AI, computer vision and robotic guidance; and evaluate models in simulated
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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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projects. Contribute to PA/QA estimates for future project bids. Line management and development of other group members where appropriate. The successful applicant must be educated to degree or higher in a
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methodological and biological factors—such as the choice of substitution models, site saturation, and the role of selection, mutation, and recombination—that shape evolutionary rate estimates. A key goal is to
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travel, meetings and seminars. The ability to organise and prioritise a varied workload to meet deadlines is essential, along with a strong attention to detail. You should also possess strong computer
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collaboration with the Translational Gastroenterology Unit (TGU) and the Ludwig Institute of Cancer Research (LICR) we aim to develop a computer guided endoscopy image recognition system that will support