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coding sequences (CDS) and their cognate 3’ untranslated regions (3’UTRs) are differentially expressed in development and disease. Notably, the Nanog 3’UTR functions as a long non-coding RNA to promote
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superb quantitative background, strong coding skills (e.g., Python, R), expertise in infectious disease modeling across multiple pathogens, expertise with large datasets and statistical analysis, and high
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sensor integration. Strong coding and debugging skills. Excellent communication, documentation capabilities and a demonstrated track record of publication. An enthusiasm for developing new measurements
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accomplishments, (b) Your broader research interests, and (c) why you are interested in working with us A sample of data analysis code (published or unpublished) A representative writing sample (published
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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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with electronic health record (EHR) and/or clinical data. Proficiency in Python, with strong coding and debugging skills. Experience with deep learning frameworks such as PyTorch, JAX, TensorFlow
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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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latent variable models (especially factor analysis, item response theory, and growth modeling) and coding in R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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-author manuscript with related analysis code and references later during the recruitment process. Stanford is an equal opportunity employer and all qualified applicants will receive consideration without
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Qualifications: • Doctoral degree with quantitative training (ideally in econometrics) or relevant research experience. • Strong coding skills in R, Stata, or other statistical software package. • Good