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and model vadose fluid transport in the deep vadose zone (10s to >100 m depth) in California's Central Valley. The research will focus on the use of geophysical tools to parameterize and validate
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and apply methods for analyzing genomic variations, e.g. SNV, SNP, SV, CNV, methylation, and gene expression Develop tools using machine learning and AI Prepare high-impact manuscripts for publication
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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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