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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
and Experience Requirements A Ph.D. with an emphasis on remote sensing. Required Qualifications, Competencies, and Experience Required Qualifications for Remote Sensing Post-doc: 1. A PhD degree with an
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Geohazards risk assessment and management via remote sensing Energy geotechnics Advanced experimental testing and field instrumentation Research Project: The successful applicant will lead a fascinating
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tools Supervising and guiding Master and PhD students Active participation in project meetings and events Presenting and publishing the research on an international stage Your Profile: As part of our
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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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campus-based role that is not suitable for remote working. Education, Qualifications and Experience Education & Qualifications: Essential Criteria An Engineering Doctorate or PhD in a relevant area of
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research skills, particularly in statistical modeling, geospatial analysis, and health metrics evaluation. Experience working with a variety of spatial datasets, including remote sensing data, for health and
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experience in geo-spatial analysis and working with remotely-sensed satellite imagery Demonstrable experience of image analysis (for example, using Google Earth Engine or PlanetScope) Experience of programming
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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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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