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methods to ultimately let dermatologists continually update multi-modal machine learning models. Our research objectives are to 1) develop novel model editing methods for multi-modal models, with a focus on
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data from mobile, wearable, and environmental sensors. The successful candidate will create and deploy models that integrate machine learning, signal processing, and large language models (LLMs) to infer
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immunity and their contribution to host defense during Clostridioides difficile infection (CDI). They will use murine models of CDI, flow cytometry, transcriptional and functional assays to define
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. We utilize novel murine models and techniques and translate finding using our large cohort of human subjects with advanced imaging of coronary artery plaque volume and features of plaque instability
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and/or pathogens (ideally Toxoplasma or related protozoa), protein biochemistry, animal models of infection, and working with omics datasets. For more information, please visit: https
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of Virginia aims to improve understanding of climate-related hazards (wildfire, drought, flood) and their impacts on natural environments and human communities using remote sensing, Earth system modeling, and
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differences in vulnerability to addiction and potential treatments for addiction. The incumbent will perform experiments which utilize rat models of addiction and support the goals of the lab under
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progression and the complex interplay between tumor cell signaling and the immune microenvironment in renal cell carcinoma. Our work integrates innovative experimental models including in vivo and in vitro
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A Postdoctoral Research Associate position in advanced MRI acquisition, analysis, and modeling is available in the Department of Radiology at the University of Virginia School of Medicine, under
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autoimmunity. Additionally, the position requires experience in molecular and cellular biology, sequencing, transgenic mouse models, and mice models of autoimmunity. For more information, please visit: https