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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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endothelial response to surgery including cell-cell interaction prediction modeling as well as characterization of the surgically triggered “genomic storm” in human PBMCs and endothelial cells. The laboratory
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. The scholar will analyze VA data, construct machine learning models (including but not limited to interpretable models) focusing on clinical outcome prediction, develop manuscripts to submit to leading journals
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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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them to develop prediction models for patient safety events. In addition to priority projects, the Postdoc will have the opportunity to work with other researchers both at Stanford and within our larger
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interfaces, and condensation surfaces under varied operating scenarios. Develop and refine performance simulation models and predictive tools to support system optimization and deployment strategies. Prepare
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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
of excellence for outbreak analytics and disease modeling, named Insight Net. This position’s efforts will focus on developing predictive and analytic models of infectious disease and will use dynamic models and
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informatics is desirable. Required Qualifications: Ph.D. with a strong background in biomedical informatics (e.g. information extraction from electronic medical records and predictive modeling) Proven track