102 software-defined-network-postdoc Postdoctoral positions at University of Washington
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has four broad research areas: the solid earth, surface processes, geobiology, and space/planetary sciences. The group involved in the postdoc project are also part of the cross-campus Astrobiology
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. of Earth and Space Sciences, which has four broad research areas: the solid earth, surface processes, geobiology, and space/planetary sciences. The group involved in the postdoc project are also part of
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, single-cell multiomics, tissue engineering, and animal models. Our current research primarily focuses on four key areas: 1) Developing robust, chemically defined differentiation protocols to generate
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junior PI, I am committed to providing personalized mentorship and supporting your career development through active guidance, networking, and publication opportunities. Our research integrates
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vulnerability or survival during neurodegenerative conditions in vivo. Responsibilities of the postdoc will include, but are not limited to: in vivo imaging of biosensors to read out diversity and dynamics
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variety of experimental data, utilize different model structures/modeling techniques, are often closed source or coded in proprietary software packages with poor interoperability, and process experimental
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Position Summary The Ghanbarpour Laboratory, located in the Department of Biochemistry and Molecular Biophysics at WashU Medicine, is now recruiting for a highly motivated Postdoc. Our research is
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tumor progression. Specifically, we investigate how transcriptional regulators and gene networks govern immune and brain cell behavior. Our lab uses a broad array of approaches, including flow cytometry
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: Information on being a postdoc at WashU in St. Louis can be found at https://postdoc.wustl.edu/prospective-postdocs-2/ . Information on the DOLF project can be found at https://dolfproject.wustl.edu . Trains
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models