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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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biochemistry, genomics, molecular genetics, cell biology, and model organism systems to uncover the mechanisms by which histone mutations disrupt human development and lead to disease. Our ultimate goal is to
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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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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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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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, 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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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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. 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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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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, 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