89 phd-rehabilitation-engineering-computer-science Postdoctoral positions at Stanford University
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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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faculty, PhD students and researchers. The ideal candidate will have earned a Ph.D. in applied science and engineering discipline, with demonstrated expertise in a complementary area (e.g., a Ph.D. in
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expansion of a novel gene editing delivery technology. This is a unique opportunity to unlock the vast potential of diverse marine organisms—including corals, sea stars, hemichordates, and tunicates
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external) Candidates from a diverse background are encouraged to apply. The applicant may hold a PhD either in physical sciences/engineering with a strong interest in translational research and motivation
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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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, Stanford PRL), career development resources, and competitive benefits and salary commensurate with experience. Required Qualifications: PhD in physics, electrical engineering, mechanical engineering
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. Required Qualifications: PhD in statistics, economics, computer science, operations research, or related data science fields Strong data science skills, including experience working with large, complex data
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presentations). Evidence of their contributions to their current research communities. Track record of mentoring more junior scholars. Required Qualifications: PhD in computer science, electrical engineering
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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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in Neuroscience, Biomedical Engineering, Computational Biology, or a related field. Strong background in signal processing, including neuroimaging and/or electrophysiology (EEG, MEG) data analysis