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analyze single-cell RNA sequencing (scRNA-seq) datasets using biostatistical and bioinformatics approaches. Conduct molecular, cellular, biochemical, and immunological analyses to support research
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proteostasis, cytoskeletal dynamics, and ferroptotic cell death. This collaborative work combines biochemistry, chemical biology, cell signaling, live-cell imaging, and organotypic brain slice models to dissect
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. The Sarantopoulos Lab is seeking a B-cell enthusiast. We are using primary patient samples and mouse models to understand the role of B cells in anti-leukemia/tumor responses. For over a decade, the lab
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. Postdoctoral Associate Positions in Regeneration Genomics Diao Lab, Duke University School of Medicine The Diao Lab in the Department of Cell Biology at Duke University School of Medicine has multiple openings
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, genomics, or computational analysis of high-dimensional datasets. Experience with one or more of the following: Flow cytometry Single-cell sequencing Spatial transcriptomics Multiplex imaging Bioinformatics
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technologies to study the behavior of tissues at single cell resolution. We offer an inspiring intellectual, collaborative, and multidisciplinary research environment to support your career goals and provide
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background in molecular biology and cell biology. Experience working with animal models. Demonstrated scientific curiosity, initiative, and commitment to research excellence. Ability to work independently
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, and other relevant cell types. Conduct stereotaxic surgical procedures and targeted injections in mouse models. Support viral vector delivery and molecular, biochemical, histological, and cellular
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for Duchenne muscular dystrophy (DMD), funded by NIH R21 project (1R21AR087645-01). The project aims to target pathogenic inflammatory and fibrotic stromal cell populations that impair muscle regeneration in DMD
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candidates will also have demonstrated experience in at least one of the following areas: chemical biology, microbiology, cell biology, pathogen disease research, computational approaches to drug design