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for data-efficient vision foundation models. Foundation models in computer vision currently rely on massive datasets and brute-force scaling. This leads to high data requirements, hidden biases, limited
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expertise in artificial intelligence, computer vision, human-computer interaction, and psychology. Its technical core lies in developing robust and adaptive visual speech recognition models. Close
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at the intersection of computer vision, micro-electronics analysis, and hardware security, and will work under the supervision of researchers within the Department of Intelligent Systems. The PhD researcher will be
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of novel volumetric representations and computational imaging methods for paired visible-light and X-ray images, as well as extending computer vision techniques from the visible-light RGB photography domain
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the development of novel volumetric representations and computational imaging methods for paired visible-light and X-ray images, as well as extending computer vision techniques from the visible-light RGB
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: Completed, or soon-to-be completed MSc in the biological sciences or different fields in the natural sciences (e.g. computational, mathematical, earth or marine sciences) with a strong interest in ecology and
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(ICOS), and become part of a vibrant research environment. What are you going to do? As a PhD candidate, you will develop an interdisciplinary research programme together with a postdoc researcher
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or digital approaches: low-cost key-enablers for democratizing state-of-the-art healthcare globally. We realise this vision by combining fundamental research with systematic platform and assay development
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at the intersection of AI, deep learning, computational neuroscience, and vision science. You'll develop biologically realistic neural networks to understand how individual differences in the brain shape perception
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1 January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for PhD students; multiple courses on topics such as leadership for academic staff; multiple