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validating deep learning models for the prediction of disease progression from ophthalmic data. Skills include working with image or computer vision-based toolkits, development of multimodal, multidata type
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collection of over 9,000 pairs of donatedhuman eyes – complete with full genetic profiles, images, and ophthalmic and medicalhistories – and a large study cohort of AMD patients. The Sharon Eccles Steele
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team that investigates the the genetic and molecular mechanisms of addiction in a multi-disciplinary approach including genetics, behavior, circuit analysis, imaging, hi-throughput genomics, and
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-of-the-art methods for evaluating treatment effects from randomized and observational data sources that are subject to multiple forms of bias due to, for example, missingness, censoring, irregular assessment
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collaborative efforts among researchers at the University of Utah and UC San Diego in developing and applying methods in predictive and causal modeling of complex biomedical and social processes and systems
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is extended to qualified applicants, upon request and consistent with University policy and Utah state law. Upon request, reasonable accommodations in the application process will be provided