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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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or Biomedical Engineering or a closely related field. Preferred Qualifications: PhD in medical applications of physical sciences and engineering. Experience in medical imaging, breast x-ray imaging, image
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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
cycles. Understanding these complex interactions requires measurements that go beyond traditional multi-spectral imaging. Imaging spectroscopy—capturing the full contiguous spectrum from the Visible
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: https://www.jobbnorge.no/en/available-jobs/job/294558/phd-research-fellow-in-deep-learning-for-imaging-of-marine-ecosystems Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/294558/phd
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). Proven experience in scientific programming and numerical computing (MATLAB, Python, or C/C++), including implementation of reconstruction algorithms and image processing pipelines. Experience with
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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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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of your PhD, you may still apply if you have submitted your PhD thesis for doctoral degree evaluation within the application deadline. Demonstrated research experience in optical imaging, quantitative phase
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-management) and direct-conversion detection pathways. The role will coordinate materials/device fabrication, characterization and imaging validation (e.g., MTF/SNR, low-contrast detectability), align workplans