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Field
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Systems Engineering (Health Systems Data Analysis, Modeling, Computing, and Cloud-Based Health Systems Analytics and Decision Support Platforms) as part of the CTECH Postdoctoral Fellows program. This
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at the intersection of active transportation safety and AI (e.g., near-miss detection, predictive crash risk, multimodal exposure estimation). · Design, implement, and validate AI pipelines (computer vision
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computational science expertise. The Computational Science (CPS) Division focuses on solving the most challenging scientific problems through advanced modeling and simulation on the most capable computers
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communities using model and non-model organisms. The individual is required to use tools in chemical ecology, genomics, molecular biology, and AI-driven predictive modeling in the research project. Specific
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quantitative live-cell imaging to probe and model these processes. By combining stem cell biology with cutting-edge microscopy and physical concepts, we aim to establish a predictive framework for tissue self
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in
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(that might predict learning and outcomes), underlying cognitive and neurobiological mechanisms, and intervention outcomes using tools including, but not limited to brain imaging (broadly defined
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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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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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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