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

Description
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 September 01, 2026 or as soon as possible hereafter. The submission deadline is 2 August, 2026. Note that we can only consider applications submitted through the official university website.

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 developing novel machine learning methods for ecosystem modelling. The successful candidate will contribute to the mission of the GWC by supporting the modelling of greenhouse gas fluxes from remotely sensed multimodal data and ground-level measurements. Topics of interest include deep learning, hybrid modelling, self-supervised learning, and 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.

Programming skills in Python and PyTorch are required. Experience in working with remote sensing data (e.g., using GDAL, Rasterio, GeoPandas) and in differentiable programming is a plus.

The PhD student will collaborate closely with an interdisciplinary team at the GWC 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, Se e-mailadresse på jobportal.ku.dk , DIKU. Co-supervisor is Assistant Prof. Nico Lang, Se e-mailadresse på jobportal.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 ( 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 ( Se e-mailadresse på jobportal.ku.dk ).

The PhD programme

Qualifications 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

PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.

Salary, pension and terms of employment are in accordance with the agreement between the Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. Depending on seniority, the monthly salary starts at approximately 31,800 DKK/Roughly 4.200 EUR (August 2026 level) plus pension. The position is covered by the Protocol on Job Structure.

Responsibilities in the PhD programme

1. Carry through an independent research project under supervision
2. Complete PhD courses corresponding to approx. 30 ECTS / 1⁄2 FTE
3. Participate in active research environments, including a stay at another research institution, preferably abroad
4. Teaching and knowledge dissemination activities
5. Write scientific papers aimed at high-impact journals
6. Write and defend a PhD thesis on the basis of your project

We are looking for the following qualifications:

1. Professional qualifications relevant to the PhD project
2. Strong interest in the relevant topics
3. Academic background (e.g. courses taken) in the relevant topics
4. Good English language skills
5. (Optional) Relevant publications
6. (Optional) Relevant work experience

Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking 'Apply' below.

Please include:

1. Motivated letter of application clearly mentioning the project you are applying for (max. two pages)
2. Curriculum vitae including information about your education, experience, relevant courses, language skills, names and email addresses of three referees, and other skills relevant for the position
3. Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
4. Publication list (if possible)

The deadline for applications is 2 August 2026, 23.59 CET.

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process
After the deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified whether you will be passed for assessment.

The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at:

Interviews with selected candidates are expected to be held shortly after the submission deadline.

Questions
For specific information about the PhD fellowship, please contact the relevant supervisors whose emails are provided above.

General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website .