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and application of advanced computed tomography methods, with a focus on photon-counting CT, quantitative image analysis, and machine learning. The position will involve work across several NIH-funded
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create objective, reliable, and scalable methods for measuring behaviors that support communication, learning, social engagement, and developmental outcomes for children and families. What You'll Do
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Bayesian meta-analyses. This position also provides opportunities to develop innovative statistical methods related to clinical trial design, variable selection in high-dimensional data, prediction
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. Advanced Models and Modeling Methods. While progressing on core modeling projects such as those above, the new researcher may also participate in ongoing and upcoming projects that involve less developed
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-conscious methods. HARP is led by Dr. Jana Schaich Borg, Associate Research Professor in the Social Science Research Institute. The core development team includes Dr. Rick Hoyle, Professor of Psychology and
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/or quantitative methods expertise. Experience with implementation science. Experience using statistical or qualitative software. Other Requirements: Full-time, one-year fellowship (renewable based
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will lead the execution of the experimental plan, work on the design of the data analysis methods, and perform the appropriate computational analyses. You will perform literature searches and use them
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
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activities. Create scientific figures, model visualizations, and technical documentation. Contribute to methods development and reporting for peer-reviewed publications and conference submissions. Preferred
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of epidemiologic methods, observational study design, cancer outcomes research, population health, and health disparities research. Experience analyzing large secondary datasets, claims data, registry data, EHR data