The EcoVision Lab, led by Prof. Jan Dirk Wegner, in the Department of Mathematical Modeling and Machine Learning (DM3L) at University of Zurich (UZH) is seeking applications for two Doctoral Candidates with a strong background in computer vision and machine learning for developing novel deep learning methods for estimating forest parameters across the Arctic and the Alps over the last decades from historical remote sensing imagery as part of a joint project collaboration with the Remote Sensing of Environmental Change (RSE) group at UZH led by Prof. Livia Piermattei (see open RSE jobs here).
We offer an exciting and stimulating environment to study and work in. The University of Zurich has several internationally recognized research groups dedicated to data science, machine learning, remote sensing, historical aerial image interpretation, forest analysis, and more broadly ecology. We also collaborate with several other institutions and companies in the fields of computer vision, machine learning and earth observation, in Switzerland and abroad. The EcoVision Lab is a member of UZH.ai, the ETH AI Center, the UZH Digital Society Initiative, the UN-ETH partnership, and the ETH for Development Center (ETH4D).
We are looking for two highly motivated PhD candidates to join a Swiss project consortium of 1x PostDoc and 3x PhDs across two research groups at UZH aimed at transforming how forest analysis across the Alps and Arctic is accomplished for the last decades. The project's goal is ambitious: using historical satellite imagery to build a harmonized time-series of high spatial resolution of forest parameters like canopy height back to the 1970s. The objective is to gain a better understanding of forest change across the Alps and the Arctic to eventually inform policymakers and support environmental decision-making internationally.
Why This Project Matters
The project will develop a harmonized, open, and scalable workflow for generating high-resolution, multitemporal spatially continuous canopy height maps (CHMs) at regional-to-biome scales. It will deliver 5 m ground sampling distance (GSD) CHMs for 2000-2025 at five-year intervals, with annual products to capture rapid forest dynamics and extend CHM predictions back to the 1970s (at 10 m GSD) by integrating Landsat with declassified stereo satellite imagery. This will establish the first multi-decadal baseline of canopy height and more forest parameters, enabling to disentangle natural variability from anthropogenic trends and detect changes in forest growth, productivity, degradation, and regeneration. Implementation of the workflow will begin in the Alps, leveraging Switzerland's exceptional validation resources (LiDAR, aerial imagery, field data), before scaling to boreal forests worldwide (~12 million km2), which is an essential region to the global carbon cycle and is undergoing rapid climate-driven transformation.
Within the SNSF project, the EcoVision Lab will focus on advancing forest parameter estimation, particularly canopy height, at the most detailed level. As a PhD candidate, you will develop novel deep learning and computer vision methods to transform large-scale remote sensing imagery of different satellite missions to maps of canopy height, and further forest parameters and their change over time.
Your research will include:
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Developing deep learning models for satellite image time-series analysis and domain adaption
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Developing deep learning models for (guided) super-resolution of historical satellite imagery
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Producing calibrated uncertainty estimates for all model outputs
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Training models on heterogeneous data sources (e.g., Landsat, Sentinel-2, SPOT, Corona) and exploring multimodal combinations of different data sources.
Research Freedom & Methodological Innovation
The project offers significant freedom to explore impactful methodological directions in modern AI, including: self-supervised learning, multimodal learning, (guided) super-resolution, uncertainty estimation, time-series regression. We aim for high-impact publications both in machine learning venues (e.g., CVPR, ICCV, ECCV, ICLR, NeurIPS) and leading interdisciplinary journals such as Remote Sensing of Environment, ISPRS Journal, and Nature Sustainability.
Why Join?
These 2x PhD positions offer:
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Become part of the EcoVision Lab, a vibrant, exciting, fun place to do research on deep learning for applications to ecology
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Close collaborations with leading research groups in machine learning, computer vision, data science, remote sensing, and historical remote sensing image interpretation.
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A unique opportunity to combine cutting-edge AI research with real-world environmental impact for a yet completely under-explored research topic
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Access to diverse, large-scale historical satellite image archives
We are looking for highly motivated candidates who are excited about pushing the boundaries of machine learning while contributing to meaningful environmental impact.
You are curious, rigorous, and enjoy developing both new ideas and high-quality research software. You are comfortable engaging with challenging problems and collaborating across disciplines.
An ideal candidate will have:
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An excellent Master's degree (M.Sc. or equivalent) in Computer Science, Machine Learning, Data Science, or a closely related field (e.g., Electrical Engineering, Applied Mathematics)
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A strong foundation in mathematics and machine learning
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A lot of programming experience, preferably in Python
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Strong prior experience in deep learning and computer vision
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Interest in applying advanced ML methods to ecological and geospatial data
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Fluency in English (written and spoken) is required
Experience with topics such as self-supervised learning, domain adaption, transfer learning, multimodal learning, uncertainty estimation is a plus - but not strictly required.
We are committed to building a diverse and inclusive research environment. We encourage applications from candidates of all backgrounds and particularly welcome those who may not meet every listed criterion but bring strong motivation and potential.
Please submit your complete application via the link below, including:
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A motivation letter
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Curriculum vitae
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Academic transcripts (school and university)
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Contact details of at least two referees
The application deadline is 16 August 2026, with a planned starting date of 1 January 2027.
The contract is expected to be fixed-term for an initial period of one year, with the option to extend it up to a maximum of four years in total.
Review of applications will begin immediately and continue until the position is filled. Early applications are therefore strongly encouraged. Please reach out to Prof. Jan Dirk Wegner for any questions.
