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, multimodal transportation systems, network modeling, etc. Duties will include conducting original research, disseminating research findings, supervising undergraduate research students, managing projects
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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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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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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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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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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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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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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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involve sensor design, including analog front-end design, light source modulation, sensor miniaturization including PCB design, sensor testing via physiology-mimicking phantoms, animal models, healthy
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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