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Field
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities
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, they must ensure a sufficiently high level of accuracy to guarantee the reliability required for systems demanding high durability, reliability, and safety. The scientific vision of DuraSyS-PAC is to address
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computer vision, where the PhD project was fully or substantially method-focused on computer vision and/or AI-based image or video analysis have very strong knowledge of machine learning, with practical
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historians Integrate top-down LLM hypotheses with established bottom-up vision pipeline (existing: dhSegment/Eynollah line extraction, damage detection models, 280M+ character image database) Assist in
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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of identifying therapeutic strategies to preserve vision. We are inviting highly motivated candidates with expertise in gene regulation, stress response pathways, and cell survival mechanisms using advanced/modern
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in vision science. The post arises as a follow-on from a UKRI and Wellcome Trust funded project that was part of the “Physics of Life” initiative, bringing together researchers from the physical
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/ Location Main Campus Address 1235 E. James E. Rogers Way, Tucson, AZ 85721 USA Position Highlights The postdoctoral scholar will conduct research into hyperspectral imaging of geological and mining materials