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architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating or testing deep learning models
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integrates custom neural architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating
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cross-sectional data and the factors that underlie the trajectories of those pathways. These inferred pathways then can be empirically tested in longitudinal studies. Strong applicants will have
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; advanced image analysis; data assimilation and inference techniques for predictive modeling; multimodal data integration and analysis; applied machine learning; high performance computing techniques
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; biomedical digital twins; advanced image analysis; data assimilation and inference techniques for predictive modeling; multimodal data integration and analysis; applied machine learning; high performance
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cardiovascular epidemiology; comparative effectiveness, causal inference, and rigor and reproducibility. Multiple positions may be hired across epidemiology and biostatistics. Potential opportunities include