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microscopy, optogenetics, or in vivo imaging techniques. Experience with electrophysiological recording methods, including patch-clamp techniques. Programming and data analysis experience using MATLAB, Python
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; or of medicine/health disparities scholarship Further referred but not required background: statistical and survey methods, qualitative interviewing, and proficiency with Qualtrix or similar software or interest
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publication record in electroencephalography (EEG), in particular using source localization methods. • Enthusiasm for interdisciplinary collaboration. • Excellent publication record. • Proficiency with MATLAB
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, with social-and environmental-based methods and techniques as operative instruments, as well as the development and application of advanced urban data science and geospatial information technologies
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in high-quality scientific outlets. Your daily work will typically involve a combination of independent research and collaboration in project teams, where you help develop concepts, methods and
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disease management and pain mechanisms and methods under the mentorship of renowned faculty. The successful candidate will have the opportunity to advance their research skills, engage in interdisciplinary
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translational diagnostics. This position combines wet-lab and computational research, with opportunities to lead projects, develop new methods, publish first-author work, and collaborate across academic, clinical
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among guests with food insecurity and hypertension. This is a longitudinal, prospective natural experiment with a mixed-methods RE-AIM process evaluation, conducted with community partners in the Fort
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methods (e.g., variational autoencoders) applied to biomolecular dynamics data. ● Familiarity with elastic network modeling (ANM/GNM) or the ProDy software package. ● A track record of independent
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Hill seeks a computational postdoctoral researcher to develop and apply methods for integrating single-cell sequencing and imaging data within controlled cell-line model systems. The project focuses