Support the programme of satellite-based environmental analytics for cities in Asia and the Pacific, focusing on urban heat, land subsidence, and air-quality analysis using open Earth-observation data.
Proficiency in Python for geospatial work (e.g. rasterio, geopandas, GDAL, xarray, numpy) is required. Working knowledge of a GIS platform (QGIS or ArcGIS) and of remote-sensing fundamentals (optical and radar) is required. Familiarity with an Earth-observation data platform (Google Earth Engine, Copernicus/Sentinel Hub, or NASA Earthdata) is an asset. Familiarity with InSAR processing (SNAP, ISCE2, MintPy or comparable) is a strong asset and is essential for the land-subsidence component. Experience with cartographic production and map quality assurance is desirable. Strong attention to reproducibility, data provenance and documentation is desirable. Familiarity with the responsible use of AI tools to improve work quality and efficiency, in line with UN guidelines, is desirable.
Be enrolled in, or have completed, a graduate school programme (second university degree or equivalent, or higher) related to remote sensing, geoinformatics, geodesy, Earth observation, GIS, geography, environmental science, or related disciplines; or Be enrolled in, or have completed, the final academic year of a first university degree programme (minimum bachelor's level or equivalent) related to remote sensing, geoinformatics, geodesy, Earth observation, GIS, geography, environmental science, or related disciplines.
