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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 4 hours ago
or territorial studies. Preference will be given to candidates with aptitude for working in disaster risk management environments supported with GIS and Machine Learning, basic programming skills (e.g., Python, R
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public events. Independent work as well as collaboration with other members of the project team. Content of the research activity: Spatial analysis in GIS (e.g., in QGIS or Python) aimed at analysing
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activity: Spatial analysis in GIS (e.g., in QGIS or Python) aimed at analysing spatial relationships between data represented as vectors and rasters. WE REQUEST Professional education, qualifications and
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processes causing consecutive landslides will be undertaken. Training The individual joins a team of international experts who will support through training in remote sensing and GIS, field geomorphic
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may include site visits and data collection. Experience in numerical modelling, GIS, or hydrodynamics is desirable, but not essential and training will be provided. Prior research experience and
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techniques such as thermal drones and AI modeling, with Python, R-Studio, Yolo, SLEAP, LabGym. The applicant must have proficiency in RStudio and GIS tools, as these skills are essential for data analysis and
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for GIS, cartographic maps, geodata infrastructures and geo-analytical workflows; some experience with AI and machine learning methods to label texts (NLP) or data sources; strong programming skills (e.g
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or habitats knowledge of data analysis, statistical modelling or remote sensing experience with GIS, programming (R/Python) or handling large datasets demonstrated interest in method development or biodiversity
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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analysis, statistical modelling or remote sensing experience with GIS, programming (R/Python) or handling large datasets demonstrated interest in method development or biodiversity research Great emphasis