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Field
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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position in big data analysis, substance use, neurocognition, and tobacco regulatory science. The position entails research on (1) analysis of big data using advanced statistical methods, (2) assessment
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Institute (CVRTI), is seeking a motivated postdoctoral fellow to work on projects that bridge from basic cell biology to small and large animal model systems. The focus of the lab is GJA1-20k with application
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statistical analyses on large datasets to provide biological insights. Integrates mass spectrometry imaging data with other multimodal imaging techniques (e.g., spatial transcriptomics) for comprehensive
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Experience with the analysis of large-scale genomic data Demonstrated ability to summarize scientific findings in the form of written manuscripts and oral presentations Desired Qualifications: Interest in
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focus on the comparative effectiveness of drug therapies on dementia-related outcomes using large-scale observational electronic health record data and local electronic health record data. The fellow’s
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research, analyze data, and contribute to scholarly publications. You'll also prepare and present research findings, develop grant proposals, and mentor student researchers. Additionally, you'll have the
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imaging data. Conducts git versioning control of R-based lab-wide scripts for basic statistical analysis and preprocessing. Performs statistical analyses on large datasets to provide biological insights
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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informatics and signal processing. Experience with large volumes of physiological and temporal medical data and time series analytics are essential. Strong programming skills in Python and modern machine