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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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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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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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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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based on predictions from statistical and machine learning models Postdoctoral scholars are represented by UAW 4121 and are subject to the collective bargaining agreement, unless agreed exclusion criteria
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collaboration that aims to refine our understanding of the controls on ocean carbon cycling and ecosystem resilience with integrated observations and modeling. The lab houses a state of the art mass spectrometry
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core facilities, performs RNA seq data analysis, and studies nerve response. This role collaborates with Dr. Rutherford and Dr. Shokeen as well. The job includes taking care of the animal models, 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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environment with potential exposure to biological and chemical hazards. The individual must be physically able to wear protective equipment and to provide standard care to research animals. Salary Range: Base
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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