86 formal-verification-computer-science Postdoctoral positions at Stanford University
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embryos This Human Frontier Science Program (HFSP) (link is external) funded project is in collaboration with the labs of Hervé Turlier (CIRB-CNRS) and Chema Martin (Queen Mary University of London). We
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is $76,383. Position Title Postdoctoral Research Associate, Vascular Biology Position Description The Spin research lab at Palo Alto Veterans Health Care is seeking a Postdoctoral Fellow to be hired
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the Department of Radiology, Division of Interventional Radiology, at Stanford University is seeking a highly motivated postdoctoral fellow with expertise in biomedical engineering or a related field. The lab
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University. The ideal candidate will have a strong background in engineering—biomedical, electrical, or mechanical—with expertise in optics, imaging systems, or device development. Our research focuses
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decisions are made under pressure, and how technology can support (rather than hinder) patient care. The postdoctoral scholar will use modern data science tools and cloud computing to analyze high-dimensional
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with strong emphasis on developing, testing, and implementing optimized control or design strategies for water systems. They should have documented experience developing computational tools in water
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immunologic skin diseases. Candidates are welcome from various interrelated backgrounds, such as epidemiology, computer science, public health, health services research/health policy, and/or biostatistics
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different disciplines and mentors Stanford Departments and Centers: Medicine, Biomedical Informatics Research (BMIR) Biomedical Data Sciences Postdoc Appointment Term: 1 year minimum with the option to extend
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required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $76,383. Combining mass spectrometry-based proteomics and metabolomics, data science
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include, but are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance