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- Faculty of Science, Charles University
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Your position Research Area and project description The project investigates how artificial intelligence can support the achievement of healthy soils by 2050 through EU policy implementation (e.g
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seagrass data Knowledge of GIS, remote sensing or ecological modelling Experience in linking scientific research with environmental policies Scuba diving certification at the rescue diver level Ability
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components, the primary focus will be on developing and validating the scalable geospatial AI framework, with field and policy integration supported through established collaborations. You’ll gain advanced
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programme No Description/content The Graduate School for Geoinformatics (GSGI) provides a structured doctoral education in the interdisciplinary field of geoinformatics. GI (geoinformation) is a powerful
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and settlement development, regional socio-economic development and policy, social and cultural climate, and landscape. Start of studies in the winter semester (October) Opening of applications and
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, institutional analysis, and resource-use conflicts. The Departments of Human Geography, Physical Geography and Cartography, GIS and Remote Sensing are closely linked with the Centre for Biodiversity and
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: geospatial analysis / GIS; remote sensing or street-level imagery; environmental exposure assessment; epidemiology or health data analysis•Good written and oral communication skills in English•Ability to work
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, generating evidence to support long-term climate adaptation and investment planning. Students will build a comprehensive set of high-value technical and professional skills, including: • Geospatial and GIS
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(preferably in R, Python, GIS) • Competences in quantitative research methods - ideally knowledge of several of the following aspects of quantitative data analysis: analysis of large/longitudinal datasets
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experience in academic writing for publication (e.g. first or co-authored peer reviewed papers) Well-developed statistical software skills (preferably in R, Python, GIS) Competences in quantitative research