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from human blood and tissues) and analysis of -omics data, including spatial transcriptomics, to investigate the role of CD8 T cells in the cellular and molecular mechanisms of autoimmune diseases
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-cell CITE-seq, scATAC-seq, and spatial transcriptomics. 2) Perform data analyses to identify pathogenic signatures that drive mechanistic studies in our NIH funded collaborative projects. Key
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. The successful candidate will: 1) Lead the development of computational pipelines for the integration of large-scale single-cell omics datasets, including single-cell CITE-seq, scATAC-seq, and spatial
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debugging code and/or the computing environment Meeting weekly with the PI and sometimes graduate students and postdocs to learn about the MIDAS data acquisition system deployed for the experiment
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projects in AI/NLP for healthcare, including literature search, data preprocessing, and preliminary analysis. Assist postdocs and graduate students in running experiments and organizing research materials
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information delivery, and supporting feedback processes in real-world care settings. The postdoc and PI will jointly envision projects tailored to the fellow’s skillset and long-term career goals. Postdocs will
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pipelines (e.g., in Python and MATLAB) 10% Process large-scale experimental datasets from two-photon imaging, electrophysiology (spikes from neuropixel), behavior tracking, and spatial gene expression 10
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investigators inside and outside of the CU system regarding collaborations. Perform experiments independently as well as experiments with other students, postdocs to contribute to publications and overall
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faculty and research personnel. There are currently 28 full-time faculty and approximately 55 Research staff, including postdocs and visiting faculty. This position will provide essential administrative and