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and remote sensing. Experience with environmental modeling, downscaling, and data integration techniques. Salary Range $55,000 + depending on qualifications. Working Conditions May work around standard
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platforms for integrating spatial and temporal information, harnessing remote sensing data, using climate information, understanding fuel accumulation and running physics-based or data-driven wildfire
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diversity, pluralism, and individual differences. Required Minimum Education Level PhD Work Location Hybrid — Remote/On-campus Employment Category Fulltime Required Application Documents Cover Letter
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familiarity with model coupling frameworks (e.g., ESMF). Proficiency in programming and data analysis (e.g., Python, Fortran) and handling large datasets, including GIS or remote sensing integration. Strong
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scholarly excellence and will be expected to work independently. Candidates with experience in other types of satellite remote sensing data, and a desire to use EMIT data and other analytical techniques
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proficiency in languages such as R and Python. Experience in GIS, remote sensing, and processing projected climate data. Proven ability to manage multiple tasks effectively, work collaboratively in team
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team and complete projects without constant supervision. Understanding the complexity of ecological data sets and the analysis thereof is essential to the project. Also, the integration of remote sensing
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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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satellite remote sensing and chemical transport modeling to characterize air pollution and climate change, and their public health impacts and opportunities for mitigation. This position will work with the
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(remotely and in-person) on microwave experiments. Minimum Qualifications: Ph.D. in Physics or related field, strong research experience in 2D electronic systems, some nanofabrication experience. Preferred