PhD Fellowship in Machine Learning for Wetland-Based Climate Change Mitigation
This fully-funded PhD fellowship at the University of Copenhagen is part of the Global Wetland Center (GWC), an initiative funded by the Novo Nordisk Foundation. The GWC contributes to the development of wetland-based climate change mitigation strategies through a combination of biogeochemical and hydrological modelling, satellite remote sensing, and the use of artificial intelligence techniques.
The PhD position involves developing novel machine learning methods to model greenhouse gas fluxes from both remotely sensed multimodal data and ground-level measurements. To address challenges of limited reference data, the student will work on hybrid modelling combining process-based models and deep learning, as well as self-supervised learning approaches. The PhD student will also contribute to the development of new global-scale datasets. This role focuses on high-impact research projects targeting top-tier computer science publication venues and journals in remote sensing and ecology.
The PhD student will collaborate closely with researchers at the Global Wetland Center and will be affiliated with the Danish Pioneer Center for AI. Depending on the candidate, there is an option to become part of the ELLIS PhD program. Principal supervisor is ELLIS fellow Prof. Christian Igel, with co-supervisor Assistant Prof. Nico Lang.
The position is based within the Machine Learning Section, part of the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen, Denmark. The University of Copenhagen, founded in 1479, is the oldest and largest university in Denmark, ranked among the top institutions in Europe.
Key Qualifications:
- An equivalent to a Danish master’s degree in computer science, applied mathematics, geomatics, or related disciplines.
- Genuine interest in interdisciplinary research and a strong background in machine learning and computer vision.
- Experience in working with different remote sensing modalities is required.
- Programming experience in Python (especially PyTorch, GDAL, Rasterio, GeoPandas) is required.
- Knowledge in differentiable programming is a plus.
Deadline: April 06th, 2026