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of mutual interest such as, but not limited to, automatic machine learning model selection and automatically explaining machine learning classification / prediction results. The Luo lab is in UW Medicine’s
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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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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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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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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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accuracy in link-tracing designs (e.g. Respondent driven sampling) Partial graph data collection strategies for networks (e.g. Aggregated Relational Data) Large scale models for anomaly detection on graphs
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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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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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, 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