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Learning Medical Image Analysis Required Qualifications: Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering, Medical Physics, Mathematics, or a related field. Strong publication record
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(HAI)(link is external) . Research Focus The fellow will lead computational modeling efforts to develop large-scale, multimodal models of the human brain. The work will involve integrating brain imaging
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with access to expertise in neuroscience, genomics, metabolism, imaging, mouse physiology, and computational biology. The successful candidate will receive support for publications, conference
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incandescence, absorption spectroscopy, particle imaging, or light scattering. Experience with extractive measurements such as gas chromatography, mass spectrometry, FTIR, aerosol sizing, particle sampling
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cancer progression in the West lab in the Department of Pathology at Stanford. Successful candidates will use a combination of spatial transcriptomics and highly multiplexed imaging to understand how
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computational structural biology, ideally complemented by molecular biology expertise, who are excited to work at the interface of these disciplines. Lab overview: The Huttenhain lab in the Molecular & Cellular
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computational biology, cancer biology, and/or molecular biology preferred • Experience in image processing and analysis also preferred • The candidate will report directly to the Principal Investigator and will
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disorders. Key responsibilities include: (1) Develop and apply computational methods for heart failure discovery. (2) Analyze large-scale human datasets, including imaging, genetics, omics, EHR, and outcomes
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intellectual perspectives these backgrounds bring. Prof. Lynette Cegelski is Professor of Chemistry and, by courtesy, of Chemical Engineering at Stanford and is affiliated with the Stanford Biophysics Program
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) conferred by start date Demonstrated experience with imaging and/or video datasets Training and experience in machine learning, computer vision, and deep learning methods Excellent English language