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temporal scales - in both terrestrial and aquatic ecosystems, their origination and their response to global changes. To make these connections, we collect data through environmental DNA methods, remote
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forests across spatial scales - from the tree to the landscape level. We will link high spatial and temporal resolution remote sensing data with traditional ground-based forest monitoring data, novel
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are also expected. Profile PhD in Data Science, Computer Science, Mechatronics, Remote Sensing, Engineering Geology or other related discipline Demonstrated expertise in machine learning and computer vision
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apply for third party funding. Contributions to teaching within the Engineering Geology group are also expected. Profile PhD in Data Science, Computer Science, Mechatronics, Remote Sensing, Engineering
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, geochemistry, remote sensing and data science into a multidisciplinary framework to capture changes in As contamination patterns and their drivers in the Red River and Mekong deltas. Understanding such changes
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. Job description The project will develop air quality models for the cities in the case studies of MASSEV. Data from local sensor networks, measurement stations, remote sensing will all be considered