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Field
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-global) predictions of erosion patterns. In addition, the generation of new data, for example through remote sensing of suspended sediment, is encouraged and offers exciting research opportunities. The 41
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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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decision-relevant outputs such as restoration and implementation scenarios. The postdoc will collaborate closely with experts in remote sensing, ecology, environmental science, and engineering while
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, biochemical, ecological and physiological measurements and experiments with remote sensing and numerical modelling to create in-depth understanding of the processes that control estuarine and delta systems. One
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decision-relevant outputs such as restoration and implementation scenarios. The postdoc will collaborate closely with experts in remote sensing, ecology, environmental science, and engineering while
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remote sensing. Contribute to ongoing projects with respect to participation in already planned field work and data analysis (e.g., CASA, KUMO, BUMUS) and lead new initiatives. Lead the setup, deployment
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Earth system scientists to integrate field observations, remote sensing products, and existing datasets into a mechanistic modeling framework. The position offers substantial intellectual independence and
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-impact journals and present research at international conferences. Your Profile Requirements: Education: PhD in Remote Sensing, Environmental Engineering, Geoinformatics, Telecommunications, or a related
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Posted on Tue, 12/16/2025 - 13:32 Important Info Faculty Sponsor First name: Barbara Faculty Sponsor Last Name: Simpson Stanford Departments and Centers: Civil and Environmental Engineering Postdoc
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful