2x PhD Positions as part of the SNSF Project “From Alps to Arctic: Satellite-based Assessment o

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Within the SNSF project, the Eco Vision 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.

• 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.

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.

• Become part of the Eco Vision 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

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 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.

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