PhD fellowship in Machine Learning for Environmental Sciences - Department of Computer Science, Machine Learning Section

The University of Copenhagen is offering a fully-funded PhD fellowship in Machine Learning for Environmental Sciences within the Department of Computer Science. The position focuses on developing machine learning methods to model greenhouse gas fluxes using remote sensing and ground-level data. Candidates should have a master's degree in relevant fields and experience in machine learning, remote sensing, and programming. The start date is July 01, 2026, with applications due by April 06, 2026.
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PhD fellowship in Machine Learning for Environmental Sciences - Department of Computer Science, Machine Learning Section The Department of Computer Science, Machine Learning Section, invites applications for a fully-funded PhD fellowship. The position is part of the Global Wetland Center (GWC), funded by the Novo Nordisk Foundation. The start date is July 01, 2026 or as soon as possible hereafter. The submission deadline is April 06, 2026.
About The Global Wetland Center
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 use of artificial intelligence techniques. The center is a collaboration between two departments from the University of Copenhagen (Department of Geosciences and Natural Resource Management and Department of Computer Sciences), DHI A/S, and GEUS (The Geological Survey of Denmark and Greenland).
About The PhD Position
We are looking for a creative student who is interested in contributing to the mission of the GWC, by developing novel machine learning methods to model greenhouse gas fluxes from both remotely sensed multimodal data and ground-level measurements. To overcome 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 contribute to the development of new global scale datasets.
Applicants must hold an equivalent to a Danish master’s degree in computer science, applied mathematics, geomatics, or related disciplines. The position requires genuine interest in interdisciplinary research and a strong background in machine learning and computer vision. The student will work on high-impact research projects with a focus on methodological research targeting top-tier computer science publication venues (NeurIPS, CVPR, ICML, ICCV, ECCV, etc.) and journals in remote sensing and ecology.


Experience in working with different remote sensing modalities is required, as well as programming experience in Python (especially PyTorch, GDAL, Rasterio, GeoPandas). Knowledge in differentiable programming is a plus.


The PhD student will collaborate closely with researchers at the Global Wetland Center and will also 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, igel@di.ku.dk, DIKU. Co-supervisor is Assistant Prof. Nico Lang, nila@di.ku.dk, from DIKU.
Our section and research - and what do we offer?**
The Machine Learning Section is a part of the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen, and the ELLIS Unit Copenhagen (https://ellis.eu/). The University of Copenhagen was founded in 1479 and is the oldest and largest university in Denmark. It is ranked as the best university in Scandinavia and as one of the top institutions in Europe. The university is hosting the Pioneer Center for Artificial Intelligence.
The Department of Computer Science (DIKU) offers a friendly and thriving international research and working environment with opportunities to build up internationally competitive research groups. Copenhagen is one of the 10 most livable cities in the world with a rich culture within music, theater and associations. Life for families is made easy by a publicly supported daycare and health care system, dual career opportunities, maternity/parental leave and six weeks of paid annual vacation.


International candidates may find information on living and working in Denmark here. Useful information is also available at The International Staff Mobility office (ISM) at the University of Copenhagen (link). ISM offers a variety of services to international researchers coming to and working at the University of Copenhagen.


Inquiries about the department, the position and working in Denmark can be made to Christian Igel (igel@di.ku.dk).


The PhD programmeQualifications Needed For The Regular Programme
To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g.
Computer
Science, Electrical Engineering, Mathematics, Operations Research, and Statistics. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.
Terms of employment in the regular programme
Employment as a PhD fellow is full time and for a max. 3 years.
Employment is conditional upon your successful enrolment as a