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Livia Piermattei

3 months ago

PhD in Earth Observation, AI, and Computer Vision for Forest Canopy Height Reconstruction University of Zurich in Switzerland

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded 4-year PhD position funded by the Swiss National Science Foundation (SNSF). The project offers access to unique international remote sensing datasets, strong computational resources, and a supportive research environment.

Deadline

Expired

Country flag

Country

Switzerland

University

University of Zurich

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Keywords

Computer Science
Machine Learning
Environmental Science
Deep Learning
Biology
Remote Sensing
Geography
Artificial Intelligence
Photogrammetry
Computer Vision
Earth Science
Earth Observation
Statistics
Geospatial Information

About this position

University of Zurich is recruiting three PhD candidates for an SNSF-funded project on forest canopy height reconstruction from satellite data. The positions are part of the project “From Alps to Arctic: Satellite-based Assessment of Forest Canopy Height across Decades”, jointly led by Prof. Livia Piermattei (Remote Sensing of Environmental Changes Group, Department of Geography) and Prof. Jan Dirk Wegner (EcoVision Lab, Department of Mathematical Modeling and Machine Learning).

The research combines earth observation, remote sensing, AI, computer vision, and machine learning to reconstruct forest change across the Alps and Arctic using modern and historical satellite imagery. Topics include deep learning for satellite image time series, self-supervised and multimodal learning, domain adaptation, super-resolution of historical imagery, photogrammetry, laser altimetry, uncertainty estimation, and forest change analysis.

This specific advertised PhD position focuses on reconstructing forest canopy height models from optical stereo satellite imagery across alpine and boreal forests. The project uses archives such as SPOT-5, Pléiades, WorldView, ArcticDEM, ICESat-2, GEDI, and airborne LiDAR to generate multi-temporal DSMs and CHMs spanning more than two decades, and to study long-term forest structural dynamics, disturbance, growth, loss, and regeneration.

Eligibility: MSc degree in photogrammetry, remote sensing, geomatics, geodesy, physical geography, environmental sciences, computer science, geoinformatics, aero/astro engineering, or a related field. Strong interest or experience in remote sensing data processing, stereo photogrammetry/SfM, VHR satellite imagery, LiDAR or laser altimetry, geospatial data processing, and scientific programming is expected. Experience with point clouds, computer vision, machine learning, Linux, Git, Jupyter, cloud computing, and open-source geospatial tools is a plus.

Funding: Fully funded 4-year PhD position. The project provides access to unique datasets, computational resources, and collaboration with researchers across Europe and Canada.

Application: Apply via the UZH job portal with a cover letter, CV, publication list (if any), master certificate, master thesis or draft, and contact details for two referees. Applications are reviewed from 17 August 2026 until the position is filled. Start date is 1 January 2027 or by agreement.

Funding details

Fully funded 4-year PhD position funded by the Swiss National Science Foundation (SNSF). The project offers access to unique international remote sensing datasets, strong computational resources, and a supportive research environment.

What's required

Applicants should hold an MSc degree in photogrammetry, remote sensing, geomatics, geodesy, physical geography, environmental sciences, computer science, geoinformatics, aero/astro engineering, or a related discipline. Strong interest or experience in satellite or airborne remote sensing data processing, stereo photogrammetry or SfM software, very-high-resolution commercial satellite image processing, airborne LiDAR or spaceborne laser altimetry (GEDI, ICESat-2), geospatial data processing, and scientific programming (Python, R, Julia, or Matlab) is expected. Optional but relevant experience includes point cloud processing, computer vision or machine learning with geospatial data, forest science, Linux, Git/GitHub, Jupyter, cloud computing, and open-source geospatial tools. Excellent written and oral communication skills are required.

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Professors

Prof.

University Name
Country flag

Switzerland

Research Interests

Climate Science

Geophysics

Earth Observation

Glaciology

Geomorphology

Prof.

University Name
Country flag

Switzerland

Research Interests

Artificial Intelligence

Statistics

Computer Vision

Environmental Science

Computer Science