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and transmission network modeling is useful but not necessary, as long as the candidate is willing to learn on the job. A list recent and past research projects on electricity market design undertaken
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: The Pain Intelligence Lab The Stanford Center for Population Health Sciences Interdisciplinary collaborations within Stanford School of Medicine Opportunities to engage with national networks in rheumatology
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chain network analysis and geospatial modeling. The successful candidate will have strong data science skills, including experience working with large, complex data from varied sources, and machine
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cell fate decisions, particularly during early neural development or during the epithelial-to-mesenchymal transition (EMT) in cancer. Our recent work reveals that coding sequences (CDS) and their cognate
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algorithmic performance. For instance, the scheduling problems that an electric grid operator faces will change daily, but not drastically: although demand will vary, the network structure will remain largely
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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research interests, and (c) why you are interested in working with us A sample of data analysis code (published or unpublished) A representative writing sample (published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