36 combustion-modelling-postdoc Postdoctoral positions at Pennsylvania State University
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to Janice Whitaker @ jmw5969@psu.edu 1-2 page/s research statement including goals for program of research over the next 5 years. One-two page personal statement describing goals for the CGNE postdoc
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across various developmental stages in knockout mouse models. Apply high-throughput molecular and cellular techniques to assess gene expression, spatial organization, and cellular diversity in
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policymakers. This exciting opportunity would support an embedded policy fellowship placement to train a Postdoctoral Scholar in the implementation and evaluation of research translation models, such as the
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must be ready to conduct research involving the design of novel behavioral interventions, conduct human studies, and build computational models to implement existing behavioral paradigms such as Just 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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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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function. We utilize a variety of approaches including biochemical, proteomic, genomic, genome editing, and mouse modeling to study these questions. Here are representative recent publications from the lab
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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