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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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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 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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project topics can be found at https://indibrain.eu/recruitment . OPEN POSITION: Project 5: Modeling Individual Brain Differences using Deep Learning & Vision Neuroscience: This project sits
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those. The IBED vision includes research encompassing experimental and theoretical approaches at a wide variety of temporal and spatial scales, i.e. from molecules and microorganisms to patterns and
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approach to ecosystems, addressing abiotic (soil and water quality) and biotic factors (ecology and evolution of plants, animals, and microorganisms), and the interplay between those. The IBED vision
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of plants, animals, and microorganisms), and the interplay between those. The IBED vision includes research encompassing experimental and theoretical approaches at a wide variety of temporal and spatial