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data, GIS, and environmental modeling. Familiarity with programming languages such as Python, Julia, R, C++, or MATLAB is considered a strong asset. Experience with fieldwork or working with soil
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, the project will enable the evaluation and optimization of resilience strate-gies. The framework will be validated through pilot studies, ensuring its applicability to real-world industrial chal-lenges
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archaeological finds. Conducting geospatial analyses in GIS, including work with LiDAR data, aerial imagery, and other remote sensing data. Developing a webGIS platform to host field-collected data, legacy
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cracking resistance as compared to GI galvanized steel. Furthermore, it is unclear at this moment how these types of coatings will perform in application to the green steel. Therefore, this project is aimed
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experience with knowledge graph standards (e.g., RDF, OWL, SHACL); familiarity with GIS, geodata infrastructures and geo-analytical workflows some experience with AI and machine learning methods to label texts
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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months or 0.8 FTE for 45 months; access to computational resources (HPC), GIS/data infrastructure, and datasets via collaborative networks; a supportive, interdisciplinary research environment within
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position of 1.0 FTE for 30 months or 0.8 FTE for 37 months; access to computational resources (HPC), GIS/data infrastructure, and datasets via collaborative networks; a supportive, interdisciplinary research
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://www.rug.nl/education/phd-programmes/prospective/phd-positions/english-language-requirements?lang=en ). Good command of Dutch. Strong digital and quantitative skills (e.g. GIS and statistical skills
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GIS and spatial analysis Data science, data mining, and information retrieval (multi-modal) Machine learning, deep learning and artificial intelligence (Initial) Experience in grant acquisition and