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Functions: Empty heading * Developing new statistical methods to model multimodal neuroimaging data, multi-omics data, electronic health record (EHR) and genetics data which are measured by longitudinally and
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) and genetics data which are measured by longitudinally and cross-sectionally. • Developing and applying machine learning and AI approaches to identify interactive topological relationships
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viruses with zoonotic potential. Using data provided by USDA influenza A virus surveillance systems: genetic evolution of IAV will be quantified; genetic predictors of influenza host range and virulence
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. The project will be carried out in a stimulating biology- and molecular medicine-oriented research environment shared with multiple principal investigators capable of attracting several important international
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multiple imaging modalities—initially concentrating on whole-body and abdominal MRI—using UK Biobank imaging data. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old
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are pioneering discoveries across diseases that have the deepest impact on our community. THE OPPORTUNITY We’re seeking a motivated Statistical Genetics Postdoc (3-year role) to play a key role in our large-scale
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Collaboration. The primary focus of this post will be the development of computational pipelines for the automated extraction and discovery of image-derived phenotypes (IDPs) across multiple imaging modalities
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convenient to be determined by both parties). The positions are fully funded for multiple years with a recent award from the National Institutes of Health on Developing Novel Technologies That Ensure Privacy
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning for improving
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motivated to move the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning