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on characterizing non-coding regulatory elements in humans and understanding how these elements change across different conditions. The project will involve developing new modeling approaches for coupling functional
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-molecule fluorescence, and bulk biochemical methods will be used to investigate how these motor proteins convert the energy from ATP hydrolysis into mechanical work in order to promote pre-ribosomal folding
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: 254037 Minimum Education and Experience: · Education: Ph.D. in Nursing from an accredited institution. · Research Experience: Demonstrated experience in research methods, preferably with expertise in
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of statistical concepts and methods. Proficiency in at least one programing language (e.g., R, SAS, STATA). Excellent quantitative analytical skills, particularly in longitudinal modeling, administrative data
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to contribute to cutting-edge research in the development of clinical trials, self-management science, acute/chronic disease management and pain mechanisms and methods under the mentorship of renowned faculty
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responsible for conducting highly specialized and technical research. The individual will be primarily engaged in the use of computational methods for compound design, including but not limited to molecular
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responsible for conducting highly specialized and technical research. The individual will be primarily engaged in the use of computational methods for compound design, including but not limited to molecular
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modeling tools such as EnergyPlus, OpenStudio, or DesignBuilder and community scale building energy tools such as UrbanOpt o Strong quantitative research skills, with the ability to leverage these methods
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using various biophysical methods (for example: EMSA, ITC, AUC, FLUORESCENCE ANISOTROPY, BLI, MST, CD etc.). Experience with assay development and molecular microbiology a plus. Research experience in
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neuroscience research with clinical applications to addiction and other mental health concerns. Our work draws on methods from psychology, experimental economics, human neuroscience, and computational psychiatry