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PhD with an interest in computational social science or data science (broadly defined). This individual will help design, execute, and analyze a series of field experiments on a platform created by the
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in the United States. Preferred methodological skills include statistical analysis of survey and other large-n data, qualitative interviews, and/or text analysis and machine learning skills
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activities such as working with large, longitudinal datasets, designing and implementing lab-based experiments, coding video data, writing manuscripts, presenting research, and mentoring undergraduate and
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disease, including AI-powered tools and new statistical techniques that leverage large datasets, heavy computational capabilities, and/or a robust understanding of biological systems to provide unique
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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variants as functional and assess their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in
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, publications and experience presenting data at national and international meetings Names and addresses of 3 academic references. One of these must be your main PhD supervisor. Stanford is an equal opportunity