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zu Berlin contributes to the development of NOMAD, the world’s largest data repository for materials science. Within SolMates, the team builds on its experience in integrating image segmentation and
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optical imaging (Leung Research Group) The Leung Research Group (https://sites.google.com/view/LeungGroup) in the Department of Intelligent Systems Engineering at the Luddy School of Informatics, Computing
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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to explore include mono-chromatic and multi-chromatic illumination, structured illumination, and telecentric illumination distortions. Computer based image analysis approaches will be developed, including
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is
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of Biomedical Engineering (SBME). The successful candidate will work on advanced machine learning applications in computational pathology, medical imaging, and clinical text analysis. They will be expected
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motivated Preferred Qualifications: Experience with serial section EM Histology Viral and immuno labeling Stereotaxic surgery Image analysis software and systems (e.g. FIJI/ImageJ, EyeWire) Light microscopy
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astrophysics previously! Candidates should hold a Master’s degree in Physics or Astrophysics, Optics, or computer science, with an interest in instrumentation, experimental work or signal processing. Experience
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cooperation partners in their research projects with state-of-the-art light and electron microscopes (https://www.leibniz-fmp.de/cellular-imaging ). We work with proteins, cell cultures, and model organisms
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Engineering, Biomechanics, Computer Science, or related field Preferred Experience: Experience with machine learning in medical imaging/biomechanics; grant writing support; clinical gait analysis in clinical