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and image annotation to support development of AI models trained on OCT scans from DME patients, including DRCR datasets. QA of segmentation of retinal layers and fluid compartments using and validating
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learning-based segmentation, multimodal image fusion, and radiomic feature extraction to construct clinically relevant prognostic models. Conducted at the Heart Institute (InCor) of Hospital das Clínicas
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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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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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such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection, classification, keypoint recognition, image registration, and image synthesis, with the goal of improving
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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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: C++, Python, Matlab, R. ● Building analytical pipelines that include image processing, image registration, auto-segmentation, feature extraction, pathomics, and radiomic analysis ● Proficiency
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Research Associate will contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods to address cutting-edge topics, including
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Reference number: 2026-0013 The Division of Computational and Molecular Prevention of the DKFZ, in collaboration with the Translational Molecular Imaging in Oncologic Therapy Monitoring Unit, invites
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.) – Alignment and processing of 3D images (ImageJ, etc.) – Volume reconstruction and segmentation – Morphological and structural analysis Specific Requirements The work will take place in collaboration with