Center for Remote sensing and Deep Learning of Global Tree Resources - TreeSense
Faculty of SCIENCE
University of Copenhagen

The Department of Geosciences and Natural Resource Management invites applicants for two PostDoc positions in Remote Sensing and Deep Learning of woody ecosystem properties.The PostDocs will be anchored in Center for Remote sensing and Deep Learning of Global Tree Resources (TreeSense) funded by the Danish National Research Foundation, and the Forsaid project (FORSAID) funded by the European Union's Horizon Europe Research and Innovation.

The TreeSense Center
The research center aims to revolutionize global tree monitoring using advanced nano-satellite technology and next-generation deep learning (DL) methods within AI. This approach will enable detailed assessment of global tree dynamics, including key functional and structural properties such as important species, the use of trees, tree horizontal and vertical structure, carbon stocks and carbon sequestration rates.

This research paves the road towards addressing science questions on major unknowns within global change research. Here the center will break new grounds on how global warming and increased climatic extreme events affect tree physiology and growth patterns at species level and we will quantify the extent and dynamics of anthropogenic forest disturbance and degradation.

Ultimately, this research enables us to uncover the potentials for various forest and tree-related production systems and human livelihoods as means of climate mitigation actions while improving our understanding of the importance of woody resources for sustainable food systems.

The role of the PostDocs will be to develop research techniques for improved assessment and monitoring of woody vegetation ecosystem properties at the level of single trees based on relevant remote sensing technology and AI algorithms, with a focus on species mapping as well as disturbances and change dynamics of trees both inside and outside forests.

Specifically, the first position will work on advancing large-scale, remotely sensed mapping of forest carbon dynamics. The position focuses on integrating state-of-the-art satellite-derived biomass, forest structure, and land-use change products to improve spatially explicit estimates of forest carbon stocks and fluxes across multiple decades. Using data-driven modelling approaches, the postdoc will refine regional- to continental-scale carbon budget assessments and explore how forest management, natural disturbances, and climate extremes shape the long-term trajectory of the forest carbon sink of Europe and possibly beyond depending on data availability/quality. The position aims to produce independent, observation-based constraints on forest carbon dynamics that are directly relevant for climate mitigation policies, greenhouse gas reporting, and biodiversity conservation at the European level.

The position is open from October 15st 2026 or as soon as possible thereafter and will be for a duration of 36 months.

The second position focusses on developing next-generation methods for mapping tree species composition across European forests using satellite and aerial imagery. The work will exploit and benchmark state-of-the-art deep learning architectures, including convolutional neural networks, vision transformers, and foundation models. Multi-sensor data will be integrated to resolve challenging species mixtures and structural variability. The postdoc will design robust training pipelines that leverage weak supervision based on existing species maps, national forest inventory data, and targeted field plots. Particular emphasis will be placed on model generalisation across biogeographical regions and sensor types. The resulting high-resolution species maps will form a core layer for assessing forest vulnerability and resilience. By linking species-distribution patterns with observed pest outbreaks and drought events, the project will provide new insights into species-specific risk profiles under climate change. The postdoc will also contribute to open, reproducible workflows and uncertainty quantification to ensure that outputs are usable for policy, management, and impact modelling at the European scale.

The position is open from September 1st 2026 or as soon as possible thereafter and will be for a duration of 18 months.

Formal requirements
Applicants should hold a PhD degree in Geography, Geoinformatics, Environmental Sciences, or related. We are seeking a highly motivated and ambitious individual with good interpersonal and communication skills. Fluency in spoken and written English is a requirement. As criteria for the assessment, emphasis will also be laid on previous publications, relevant experience in remote sensing and forest monitoring, as well as on programming skills (e.g. r, python). Proven experience with high-resolution imagery and machine/deep learning techniques are expected as well as proven experiences with handling and processing large image datasets.

Please indicate in the proposal if applying for the first or the second position.

Work environment
Your work place will be the Department of Geosciences and Natural Resource Management (IGN), which conducts research and education on the past, present and future physical, chemical and biological environments of the Earth and their interactions with societal and human systems to provide graduates and research in support of sustainable future solutions for society. The department has strong experience in interdisciplinary collaboration within and beyond the department.

Further information on the Department is linked at https://www.science.ku.dk/english/about-the-faculty/organisation/. Inquiries about the position can be made to Vivian Kvist Johansen.

The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

Terms of employment
The positions as postdocs are covered by the Memorandum on Job Structure for Academic Staff.

Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.

Negotiation for salary supplement is possible.

Please include

• Cover letter
• Curriculum vitae
• Research plan – description of current and future research plans
• Complete publication list
• Diplomas (Master and PhD degree or equivalent)
• Separate reprints of 3 particularly relevant papers

You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.

Københavns Universitet giver sine knap 10.000 medarbejdere muligheder for at udnytte deres talent fuldt ud i et ambitiøst, uformelt miljø. Vi sikrer traditionsrige og moderne rammer om uddannelser og fri forskning på højt internationalt niveau. Vi søger svar og løsninger på fælles problemer og gør ny viden tilgængelig og nyttig for andre.

Info
Ansøgningsfrist: 19-07-2026

Ansættelsesdato: 01-09-2026

Arbejdstid: Fuldtid

Afdeling/Sted: Department of Geosciences and Natural Resource Management