666 phd-position-for-fully-funded-reserch-in-computer-vision Postdoctoral positions in Uk
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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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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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hepatitis and liver disease. This post is funded by the National Institute for Health and Care Research (NIHR) as part of a significant research programme that leverages large-scale healthcare datasets
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on superconducting-quantum-computer science and the birth place of one of the world-leading companies building quantum computers, IQM Quantum Computers, along several other companies. You will work together with 20–40
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Materials & Composites Laboratory: https://eng.ox.ac.uk/mmclab/) in the Department of Engineering Science at Oxford University. The post is funded by the ERC Proof-of-Concept (PoC) grant: RESILIENT and is
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application of new methods, ensuring they are kept fully up to date with progress and difficulties in the research projects. It is essential that you hold a PhD/DPhil (or close to completion) in a quantitative
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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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set to deliver an ambitious research vision and where you can contribute to the training of PhD and Master’s students. You are excited about opportunities to communicate with international collaborators
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene