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and trends for Central Europe using remote sensing data” funded by the European Forest Institute. <\b> The position is suitable for disabled persons. Disabled applicants will be given preference in case
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://www.slu.se/en/about-slu/work-at-slu/ Remote Sensing for Integrative Forest Management Description: We are now seeking a PhD candidate to work on the use of remote sensing for integrative forest management (IFM
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resilience. As a PhD researcher you will: work together with an interdisciplinary team of experts in remote sensing, and plant ecology. characterize vegetation dynamics in terms of abruptness, severity and
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well as international partners. Field experiments, digital technologies -- including modelling and remote sensing, as well as interactions with stakeholders are key components of Land-CRAFT. The successful candidate will
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Description Do you want to explore GNSS reflectometry (GNSS-R) for remote sensing applications on Unmanned Aerial Systems (UAS)? The Technical University of Denmark invites applicants to a fully funded 3-year
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time periods. Coupled with other remote sensing variables (e.g. NDVI, canopy height), these datasets offer a foundation for in-depth analysis of forest dynamics. The primary challenge for this PhD
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multiple data sources with different granularities ranging from monitoring networks, field samples, weather data, crop information, remote sensing, and historical data. The PhD will be part of a team that
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connected studies into structural damage assessment. This role will: work with damage data observed and collected from the 2020 Beirut explosion (damage assessment database, specific studies and remote
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FieldGeosciencesEducation LevelMaster Degree or equivalent Skills/Qualifications Your profile: MSc degree in geoinformatics, remote sensing, geography or related fields Background in hydrology or climatology is an asset
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Skills/Qualifications Your profile: MSc degree in geoinformatics, remote sensing, geography, computer science or related disciplines Experience in optical and radar analysis Familiarity with deep learning