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with multivariate statistics, machine learning, and/or remote sensing would be an asset. Experience and education: Ph.D. degree in geography, agriculture/agronomy, environmental science, or a related
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essential to the project. Also, the integration of remote sensing data for analysis with ground-truth data will be useful. The applicant must be able to comply with high standards of work ethics, regulations
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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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, materials optimization, and microwave control are highly preferred qualifications Please feel free to apply for the position even if you do not have all the required skills and knowledge. We may be able
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including but not limited to large language models (LLM) in combination with field data and remotely sensed satellite, acoustic, camera, and genetic data have a critical role to play in driving action
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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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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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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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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