54 phd-in-mathematical-modelling-population Postdoctoral positions at Pennsylvania State University
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peer-reviewed journals Hands-on experience with mammalian cell culture, rodent models, and standard molecular/cell biology techniques (e.g., Western blot, PCR, transfection, construct design, shRNA
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population genetics. Our location in State College, Pennsylvania, is known for excellent schools, affordable living, and numerous opportunities for outdoor activities. Education and Experience A Ph.D. in
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in statistical network modeling, with applications in health and social science data. The scholar will have an opportunity to collaborate with other researchers, and mentor graduate and undergraduate
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methyl shuffling leaves a discernible isotopic imprint, regardless of the source. Qualifications and Requirements: A PhD in stable isotope geochemistry, organic geochemistry, or microbial biogeochemistry
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to the researcher’s career goals. Applicants must have a PhD in rural sociology, sociology, geography, or a related field, with experience in mixed-methods social science research in rural settings, including
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molecular biology techniques, 2D and 3D cell culture models (hydrogels), imaging techniques (TEM, SEM, confocal), protein analysis (immunohistochemistry/immunofluorescence), and handling of rodents. Specific
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or disease modeling in R and/or Python statistics data analysis communication (written and verbal) This does not mean you need expertise in these areas but rather that you have some base knowledge upon which
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experimental, modeling, and analytical characterization and analysis tasks to evaluate the utility of various materials exposed to extreme environments. The successful candidate will provide materials synthesis
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End Date: June 30, 2025 Expected Start Date: August 1, 2025 preferred but negotiable. Eligibility: PhD prepared Nurses (or related field) within two years of degree completion preferred. Demonstrated
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) develop and apply statistical genomic methods to analyze multi-omics datasets for understanding complex disease etiology and (2) develop and apply novel statistical models to analyze EHR data