PhD position: Multi-temporal forest canopy height reconstruction from satellite data

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The University of Zurich, Switzerland’s largest university, offers a range of attractive positions in various subject areas and professional fields. With around 10,000 employees and currently 12 professional apprenticeship streams the University offers an inspiring working environment on cutting-edge research and top-class education. Put your talent and skills to work with us. Find out more about UZH as an employer!

• Process and analyse large archives of optical stereo satellite imagery.
• Develop and improve photogrammetric workflows for DSM and CHM generation.
• Generate and validate multi-temporal canopy height models.
• Analyse long-term forest structural dynamics and disturbance processes.
• Publish research results in peer-reviewed journals and present them at international conferences.
• Contribute to teaching activities within the Department of Geography.

You hold a MSc degree in photogrammetry, remote sensing, geomatics, geodesy, physical geography, environmental sciences, computer science, geoinformatics, aero/astro engineering, or a related discipline.

Experience or strong interest in at least one of the following:

• Satellite or airborne remote sensing data processing/analysis
• Stereo photogrammetry and/or SfM software (open-source or commercial)
• Very-high-resolution commercial satellite image processing and/or analysis
• Airborne LiDAR, and/or spaceborne laser altimetry (GEDI, ICESat-2) analysis
• Geospatial data processing
• Scientific programming (Python, R, Julia, or Matlab)

• Point cloud processing and/or analysis
• Computer vision and/or machine learning involving geospatial data
• Forest science
• Linux, Git/Github, Jupyter, Cloud computing
• Open-source geospatial stack (e.g., GDAL, PDAL, Geo Pandas, xarray)
• Excellent written and oral communication skills (publication or other technical writing, conference poster or talk)

• A fully funded 4-year PhD position.
• Access to unique international remote sensing datasets.
• Project collaboration with leading forest and remote sensing researchers across Europe (such as WSL, TU Wien, NIBIO, and IGE Grenoble) and Canada (Canadian Forest Service).
• Excellent research infrastructure and computational resources.
• A stimulating and supportive research environment at the University of Zurich.
• Opportunity to collaborate with both remote sensing and machine learning research groups at the University of Zurich.

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