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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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identification of phases in metallic systems such as aluminium alloys or steels. You will have demonstrated expertise in applying machine learning and computer vision techniques for the analysis of scientific
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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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, machine learning, multiscale and multiphysics simulation, computational anatomy, medical image analysis, and integration of wearables and biosignal processing, applied to conditions ranging from cardiac
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
and machine learning, and for the public to see the worlds of the outer solar as they would appear to our eyes for the first time. The envisaged project includes: image selection, cleaning and
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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The successful candidates will join the Computer Vision, Machine Intelligence and Imaging research group, led by Prof. Djamila Aouada, to conduct research in Artificial Intelligence with a primary
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(but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. Experience in deep learning, machine learning and medical imaging processing Programming
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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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