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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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in human genetics and population data to experimentally testable mechanisms using powerful genetic models. Projects will combine bioinformatics, genomics, genetics, and systems biology, with
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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master
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on physiologic waveform analysis, biomedical signal processing, and computational modeling of continuous clinical monitoring data. The successful candidate will work on projects involving the analysis
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the sensitivity and information content of cell-free nucleic acid analysis, including ctDNA detection at very low tumor fractions and cfRNA-based readouts of tumor and host biology. A second arm applies these
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, including opportunities to develop new scientific directions, design and execute experiments, analyze and interpret data, prepare manuscripts, present research, and contribute to collaborations within
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design through data work, analysis, and publication, with the goal of co-authored papers aimed at leading economics and interdisciplinary journals. Contribute to ongoing lab projects or priorities based
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Proficiency in R and/or Python for data analysis is a plus Record of research productivity and publication Motivation to start or continue in benign urology research Written and verbal communication skills
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://stanfordsciencefellows.stanford.edu/apply(link is external) Stanford Energy Postdoctoral Fellowship: https://energypostdoc.stanford.edu/(link is external) Data Science Fellowship: https://datascience.stanford.edu/programs/data-science
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interdisciplinary team of physician researchers, epidemiologists, environmental scientists, biologists, and engineers working in a developing research site in coastal Eastern Indonesia. Data collection pipelines in