PhD Fellowship in Machine Learning for Environmental Sciences at University of Copenhagen University of Copenhagen in Denmark

The University of Copenhagen invites applications for a fully funded PhD fellowship in Machine Learning for Environmental Sciences, based in the Department of Computer Science, Machine Learning Section. This position is part of the Global Wetland Center (GWC), funded by the Novo Nordisk Foundation, and focuses on developing machine learning methods to model greenhouse gas fluxes using multimodal remote sensing and ground-level data. The research aims to advance wetland-based climate change mitigation strategies through biogeochemical and hydrological modelling, satellite remote sensing, and artificial intelligence.

The PhD student will work on hybrid modelling approaches, combining process-based models and deep learning, as well as self-supervised learning, to address challenges of limited reference data. The project involves creating new global-scale datasets and contributing to high-impact research targeting top-tier computer science and remote sensing venues. The student will collaborate with researchers at the Global Wetland Center, DHI A/S, GEUS, and be affiliated with the Danish Pioneer Center for AI. There is also an option to join the ELLIS PhD program.

Applicants should have a degree equivalent to a Danish master’s in computer science, applied mathematics, geomatics, or related fields, with a strong background in machine learning and computer vision. Experience with remote sensing modalities and proficiency in Python (especially PyTorch, GDAL, Rasterio, GeoPandas) are required. Knowledge of differentiable programming is a plus. Good English skills are essential. The position offers a monthly salary starting at 31,242 DKK (approx. 4,180 EUR) plus pension, and includes benefits such as paid vacation, parental leave, and public healthcare.

The application deadline is April 6, 2026. To apply, submit your application electronically via the provided portal, including a motivated letter, CV, diplomas, transcripts, publication list, and contact details of three referees. Clearly mention the project and the principal supervisor, Prof. Christian Igel, in your application. For more information, contact Prof. Christian Igel ([email protected]) or Assistant Prof. Nico Lang ([email protected]).

This opportunity is ideal for creative students passionate about interdisciplinary research at the intersection of machine learning, environmental science, and remote sensing, aiming to make a global impact on climate change mitigation.