119 algorithm-development-"Prof"-"Prof" Postdoctoral positions at University of Oxford
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opportunities for training and professional development, including supervision of students, contribution to grant applications, and collaboration with pharmaceutical partners on translational neuroimmunology
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relevant to setting a roadmap for ongoing experiments, as well as recently developed applications of tensor network techniques to large-scale partial differential equations. We are advertising two positions
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Lives with Linear Accelerators) project, which aims to leverage technologies developed for particle physics, computer vision and robotics into a novel end-to-end radiotherapy system as an essential
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scientists, forming small teams focused on ambitious, ‘blue sky’ research for novel methods development relevant for drug discovery analysis pipelines, trial design and operational efficiency. Led by Professor
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NHS Foundation Trust. The post holder will be a member of the Retinal Disease and Repair Group (Xue Lab) with responsibility for carrying out research to develop advanced therapies for inherited
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in tackling many global challenges, from reducing our carbon emissions to developing vaccines during a pandemic. The Department of Computer Science at Oxford is renowned for pioneering research and
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scientists, forming small teams focused on ambitious, ‘blue sky’ research for novel methods development relevant for drug discovery analysis pipelines, trial design and operational efficiency. Led by Professor
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About the role We are seeking a highly motivated and ambitious Postdoctoral Researcher. This position focuses on developing innovative cancer treatments, particularly within the field of antibody
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and refining theories as appropriate. You will develop ideas for generating research income, and present detailed research proposals to senior researcher as well as analyse and interpret data, and
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly