Forest canopy height is a key indicator of forest growth, disturbance, carbon storage, and ecosystem resilience. However, existing large-scale canopy height products are limited by coarse spatial resolution, restricted temporal coverage, or reduced accuracy in remote regions such as boreal forests. By integrating multi-source satellite observations, photogrammetry, laser altimetry, and advanced machine learning, this project aims to establish an unprecedented multi-decadal record of forest structural change spanning from the Alps to the Arctic.

The project is jointly led by Prof. Livia Piermattei (Remote Sensing of Environmental Changes Group, Department of Geography, University of Zurich) and Prof. Jan Dirk Wegner (EcoVision Lab, Department of Mathematical Modeling and Machine Learning, University of Zurich).

The project is funded by the Swiss National Science Foundation (SNSF) through the project "From Alps to Arctic: Satellite-based Assessment of Forest Canopy Height across Decades", and involves a network of national and international collaborators from Switzerland, Austria, Norway, France, and Canada.

The project team will consist of three PhD students and one postdoctoral researcher.

The announced PhD position is part of the Remote Sensing of Environmental Changes (RSE) group in the Department of Geography at the University of Zurich, Switzerland.
This PhD project will focus on reconstructing forest canopy height models from optical stereo satellite imagery across alpine and boreal forests. The project will exploit archives of stereo satellite imagery (e.g., SPOT-5, Pléiades, WorldView), ArcticDEM, satellite laser altimetry data (ICESat-2, GEDI), and airborne LiDAR data to generate multi-temporal digital surface models (DSMs) and canopy height models (CHMs) spanning more than two decades.

Beyond the methodological developments, the PhD candidate will use the generated canopy height datasets to investigate long-term forest structural dynamics. Research will focus on quantifying spatiotemporal forest change (growth, loss, and regeneration), characterising disturbance processes, and analysing ecosystem responses to natural and anthropogenic drivers across alpine and boreal forests.

The candidate will be based at the Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland. The position is primarily office-based, with the flexibility to work remotely one day per week.

Your responsibilities

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.

Your profile

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)

Other relevant, but optional experience (ideally one or more):

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, GeoPandas, xarray)
Excellent written and oral communication skills (publication or other technical writing, conference poster or talk)

We offer

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.

Information on your application

For any questions about the position and the application process, don't hesitate to contact Livia Piermattei.

We look forward to receiving your application with the following documents:

Cover letter (what excites you about this position and the work you have done) (max two pages)
CV (max three pages)
List of publications (if any)
Master certificate
Master thesis (or draft if not yet completed)
Names and contact information (email) of 2 referees.

Applications will be reviewed starting on 17 August 2026 and will continue until the position is filled. The anticipated start date is 1 January 2027.

What we offer

Work-Life Balance

Flexible working models (such as part-time positions, mobile working, job-sharing)
Childcare at the kihz foundation of UZH and ETH

Learning and Development

Wide range of continuing education courses of UZH and the Canton of Zurich
Language Center run jointly with ETH Zurich

Food

Food and drinks at reduced prices in the UZH cafeterias
Lunch-Check-card with UZH contribution

Healthcare

Special conditions on the Academic Sports Association ASVZ
Free seasonal flu vaccinations
Rest and relaxation at the quiet room in the university tower

Discounts

Private traffic: Carsharing, rent a vehicle, parking space
Digitalization: Hardware, software, mobile phone subscriptions
Special conditions on hotel reservations

Conditions of Employment

Policies of the UZH
Most UZH staff are employed according to public law

International Services

Support for people from outside Switzerland

Campuses

Campuses Zurich City, Zurich Irchel, Oerlikon and Schlieren
Sites Zurich West, Old Botanical Garden, Botanical Garden and Lengg

Location

Department of Geography

Winterthurerstrasse 190, 8057 Zürich, Switzerland

Further information

Questions about the job

Prof. Dr. Livia Piermattei

Working at UZH

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!

Jetzt bewerben

Erweiterte Suche

machine learning python Jobs in kanton zürich

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

Research Engineer / Research Scientist, Pre-training

Master Thesis Student. Procurement Analytics & Material Intelligence

Senior Machine Learning Engineer in Human-Centric Computer Vision

Research Scientist, Frontier AI, DeepMind

Data Engineering Consultant (w/m/x)

Wissenschaftliche/-r Mitarbeiter/-in im Rahmen der internationalen Entwicklungszusammenarbeit

Staff Data Scientist - Supply Chain Optimisation

Reinforcement Learning Expert - Humanoid (Mensch)

Vision & Perception Engineer - Humanoid (human)

Simulation & Perception Engineer Internship

Engineer III, AI/ML, Online Calibration, XR

Consultant - Forensic Technology and eDiscovery

Senior Research Scientist, Deep Learning

Praktikum Security Software Engineering und Machine Learning

Instrumentation and Test Engineer

PhD Position in AI-Supported Resilience Assessment of Climate-Neutral Transport Infrastructure

Senior Solutions Engineer

Numerical Modeling Engineer

AI Engineer | Zurich | Switzerland

Ich möchte aktuelle Job-E-Mails für Stellen als machine learning python in kanton zürich erhalten

Related Searches

Weitere Job-Standorte erkunden

PhD position: Multi-temporal forest canopy height reconstruction from satellite data 80 %