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Field
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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
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imaging, genetics, omics, EHR data, and clinical outcomes. Ongoing work builds on deep-learning phenotypes from cardiovascular imaging at population scale and extends toward myocardial tissue remodeling
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appointed through the Office of Postdoctoral Affairs. The FY27 minimum is $79, 056. Precision mapping of vector borne diseases using deep learning & high resolution remote sensing data Faculty in Stanford
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We are focused on fostering education and training in research to develop microenvironments to investigate and instruct cellular behaviour including, but not solely, stem cell differentiation. Our
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to appointment start date We welcome candidates with deep expertise in one or more relevant areas, and a strong desire to learn across disciplines: Computational Biology: Spatial/single-cell omics, bioinformatics
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depends on the background of a suitable candidate. The main topics of the group in the past few years were generative modeling, 3D reconstruction, image-editing, and deep learning using 3D data. More
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QUALIFICATIONS: PhD in computer science, electrical/biomedical engineering, statistics, applied mathematics, or a related field. Strong track record in machine learning/deep learning with imaging data
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and/or related fields including genetic epidemiology, human genetics, biostatistics, bioinformatics, computational biology, or phenomics (including EHR, deep medical imaging, and biomarker profiling