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variety of simulation and optimization techniques. Key areas of interest may include control theory, robust optimization, or distributed optimization. 2. The second candidate will focus on applied research
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substantial component of the work focuses on large scale empirical research in international macroeconomics and finance. The Global Capital Allocation Project (GCAP) Lab mixes data, economic theory, and
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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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experience building ML systems, designing and running experiments in PyTorch or JAX Strong publication record in top machine learning conferences (e.g. NeurIPS, ICML, ICLR). A strong background in theory is a
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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research interests, and (c) why you are interested in working with us A sample of data analysis code (published or unpublished) A representative writing sample (published
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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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-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