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experience in molecular and cellular neurobiology to study neuronal regeneration and preservation in retinal mouse models of injury and disease. This project will examine how cellular metabolism impacts
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genetic mutations. We aim to advance our understanding of these diseases and to develop and translate therapeutic strategies for future clinical practice. The applicant is expected to work on mouse models
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(including Crohn’s disease and ulcerative colitis) using molecular and cell biology, multi-omics technologies, murine models, and human tissues. We currently have three major focuses: 1) Innate lymphoid cells
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organoid culture, genetically engineered murine models, and human samples. The lab has successfully competed for various funding. The appointment is viewed as a training or transitional period preparatory to
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. Computational and bioinformatic skills. Experience in microscopy. Generation and analysis of mouse models. Handling of human samples. Molecular biology skills including CRISPR, cloning and qPCR. In vitro cell
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Position Summary The Ornitz Lab at WashU Medicine is seeking a highly motivated postdoctoral researcher with experience working with mouse models of development and disease. Candidates will have the
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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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multidisciplinary approach, spanning basic biology and translational medicine using mouse models and patient samples, tackles complex questions with profound implications for human health. Projects are available
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their degree in the near future) in Cancer Biology or a related field. Proficiency in experimental techniques such as cell/organoid culture, library construction, imaging and handling animal models. Experience
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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