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geospatial datasets. Integrating remote sensing datasets (e.g., optical, LiDAR, SAR) with ecological or environmental data. Proficiency in statistical computing and geospatial analysis using R and/or Python
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sciences discipline such as oceanography, remote sensing, or meteorology. • Excellent data management and computing skills. • The ability to engage with and work with a wide variety of international
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-AI is an interdisciplinary project, and a collaboration between the University of Bergen, NORCE Research and the Nansen Environmental and Remote Sensing Centre (NERSC). The candidate will work in a
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, and geospatial decision-support tools using ecological surveys, remote sensing, and airborne LiDAR data. The position offers opportunities to collaborate with government agencies, industry partners, and
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into many novel aspects of developing offshore digital twins, such as sensing, data handling, physics and AI modelling, software development, as well as experiments at the Cranfield Ocean Systems
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part of a team of UK and international scientists to quantify flood-driven hazards in these rivers. The post will involve analysis of remote sensing datasets, application and testing of numerical models
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₃ emissions affect peatland ecosystems across the island. The successful candidate will lead research integrating remote sensing, fieldwork, laboratory analysis, and geospatial methods, including GIS and
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this role, you will have: A completed PhD or master’s degree in a relevant discipline, together with relevant research experience. Strong knowledge and understanding of Aboriginal cultures and demonstrated
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energy for tropical forests. Analyze complex datasets with the NGEE-Tropics data team, including eddy covariance, forest census, plant ecophysiology, remote sensing, land use/land cover change Investigate
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, or equivalent research outputs Required Education PhD in hydrology, civil or environmental engineering, water resources, geography, environmental science, agricultural engineering, remote sensing, or a closely