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Field
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lead the image processing and computational analysis efforts, developing robust methods to register, segment, and analyse spectral micro-CT data, and — where relevant — advance reconstruction and
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of myocardial fibrosis using 68Ga-FAPI-04 PET/CT (Protein Positron Emission Tomography associated with Computed Tomography using Gallium-68-labeled Fibroblast Activation Inhibitor), integrating multimodal imaging
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working within Theme 3 of the Programme: ‘Image-guided Surgery’ and will involve the synthesis and assessment of novel multimodal nanomaterials for healthcare applications and radiolabel for PET imaging
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HCI and cybersecurity, to cancer research tools and methods for numerical analysis and machine learning. The research work takes place in a multidisciplinary team with a focus on image processing with
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Post-Doctoral Position - Medical Image Processing - (240000Y9) Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in
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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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-driven biomarkers Initial focus will be placed on projects designed to refine and validate artificial intelligence (AI) algorithms for analyzing OCT imaging in diabetic macular edema (DME). Data processing
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About the role We are seeking an outstanding candidate to work in the Process Dynamics group to contribute to advanced research in AI-driven X-ray imaging for metal alloy solidification
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
to misapprehensions about the outer planets and moons. In addition, many images were never processed in to full color and remain monochromatic. Digital image processing has now advanced significantly, permitting new
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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