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Field
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experience with spatial data analysis using GIS tools or spatial libraries and familiarity with common land-use datasets would be advantageous familiarity with environmental or agricultural economics research
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knowledge in plant species identification. The successful candidate should be able to independently conduct statistical analyses in R and hold a valid driver's license. Experience in GIS and handling large
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this ecosystem service. The student will map pedestrian networks using GIS and combine this with existing vegetation data. Field surveys will be conducted to validate the spatial data by measuring shade and
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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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science”, led by Dr Susan Hegarty. The PhD position will be focused on developing the citizen science hydromorphology framework, and is open to candidates with strong GIS skills and an academic background
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infectious diseases such as gastrointestinal (GI) infections, respiratory infections and sore throats. Using GI infections as a case study, the project will compare trends in OTC medication sales to other
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to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging through health, retail, mobility, energy and communications. Using GI infections as a case study
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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