Geospatial and Earth-Observation Intern

The Environment and Development Division seeks an intern for 4-6 months to support satellite-based environmental analytics focusing on urban heat, land subsidence, and air-quality analysis using open Earth-observation data. The internship involves Python- and GIS-intensive tasks including data preparation, geospatial analysis, validation, and cartographic quality assurance.

Responsibilities

• Assemble and quality-assure satellite and geospatial inputs, including building download manifests, verifying spatial and temporal coverage, and screening for cloud, coherence and data-quality flags (30%).
• Support the onboarding of new cities into config-driven pipelines, including sourcing administrative boundaries and population/exposure layers, authoring city configurations, and running screening analyses under supervision (25%).
• Conduct validation and reality-check work, including comparing derived values against published literature ranges, compiling ground-truth reference data, and preparing comparison tables and method-validation notes (25%).
• Support InSAR analysis for land subsidence where the candidate's background allows, including coherence diagnosis, SBAS/PS reconciliation, GNSS absolute-tie research, and preparation of supporting figures and maps to UN cartographic standards (20%).

Requirements

• 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.
• 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.

Skills

• Remote Sensing
• Geoinformatics
• Earth Observation
• GIS
• Geography
• Environmental Sciences
• Python for Geospatial
• rasterio
• Geopandas
• GDAL
• Xarray
• NumPy
• QGIS
• ArcGIS
• Remote-Sensing Fundamentals
• Optical Remote Sensing
• Radar Remote Sensing
• Earth-Observation Data Platforms
• Google Earth Engine
• Copernicus Sentinel Hub
• NASA Earthdata
• InSAR Processing
• SNAP
• ISCE2
• MintPy
• Cartographic Production
• Map Quality Assurance
• Data Provenance
• Geospatial Data Preparation
• Geospatial Analysis

Languages

English, French