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to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong programming background
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, particularly in data analysis Experience with statistical analysis (e.g., SPSS, MATLAB) and programming (e.g., R, MATLAB, Python) Experience with fMRI data collection and analysis (e.g., FSL, SPM) Required
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research experience. • Strong coding skills in R, Stata, or other statistical software package. • Good communication skills in English. Required Application Materials: CV (no cover letter or letters of
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
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/mcp.html (link is external) ) offers a world-class scientific environment and supports postdoctoral scholars throughout their postdoc with mentoring and training programs, seminar series, and annual retreats
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the effectiveness of new and innovative programs, to learn about features of programs that drive effective, to better understand the challenges to implementation and how to overcome those challenges
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measures combined with a standardized analytic pipeline applied consistently across studies, enabling biotype-based analyses and cross-project comparison. Supporting this program—and this position—are NIH
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arterial hypertension using in vivo Perturb-seq. This project is related to a new NIH-funded Program Project Grant aimed at identifying differences and similarities in gene function across vascular cell
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equivalent degree from an epidemiology, biostatistics, data science, computer science, or related programs with an interest in population health measures to apply. The scholar will join a vibrant and growing