PhD Student / PhD Student

The University of Zurich

Switzerland's largest university, the University of Zurich (UZH), offers a variety of attractive positions across various subject areas and professional fields. With around 10,000 employees and currently 12 professional apprenticeship streams, UZH provides an inspiring working environment centered around cutting-edge research and high-quality education. Put your talent and skills to work with us and discover more about UZH as an employer!

Your Responsibilities

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 from various satellite missions into maps of canopy height and other forest parameters along with their changes over time.

Your research will include:

• Developing deep learning models for satellite image time-series analysis and domain adaptation
• 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, and 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 2 PhD positions offer:

• Becoming part of the EcoVision Lab, a vibrant and engaging environment for research on deep learning applications in 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 tangible environmental impact in a largely uncharted research area
• Access to diverse, large-scale historical satellite image archives

Your Profile

We are seeking highly motivated candidates who are excited about pushing the boundaries of machine learning while contributing to impactful environmental initiatives. You should be curious, rigorous, and passionate about developing innovative ideas and high-quality research software. Comfort in tackling challenging problems and collaborating across disciplines is essential.

An ideal candidate will possess:

• 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
• Considerable 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 adaptation, transfer learning, multimodal learning, and uncertainty estimation is a plus, though not strictly required. We are committed to building a diverse and inclusive research environment and encourage candidates from all backgrounds to apply, particularly those who may not meet every listed criterion but bring strong motivation and potential.

Application Process

Apply online using the form below. Please note that only applications matching the job profile will be considered.

For further inquiries, please contact:

Nicole Trolese
HR Manager
nicole.trolese@uzh.ch