Universität Zürich · education
2x PhD Positions as part of the SNSF Project “From Alps to Arctic: Satellite-based Assessment of Forest Canopy Height across Decades”
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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:
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:
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:
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. Our employees benefit from a wide range of attractive offers. Find out more: https://www.uzh.ch/de/explore/work.html.
Your research will include:
- Developing deep learning models for satellite image time-series analysis and domain adaption
- Developing deep learning models for (guided) super-resolution of historical satellite imagery
- Producing calibrated uncertainty estimates for all model outputs
- 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:
- Become part of the EcoVision Lab, a vibrant, exciting, fun place to do research on deep learning for applications to ecology
- Close collaborations with leading research groups in machine learning, computer vision, data science, remote sensing, and historical remote sensing image interpretation.
- A unique opportunity to combine cutting-edge AI research with real-world environmental impact for a yet completely under-explored research topic
- Access to diverse, large-scale historical satellite image archives
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:
- 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)
- A strong foundation in mathematics and machine learning
- A lot of programming experience, preferably in Python
- Strong prior experience in deep learning and computer vision
- Interest in applying advanced ML methods to ecological and geospatial data
- 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. Our employees benefit from a wide range of attractive offers. Find out more: https://www.uzh.ch/de/explore/work.html.