Assistant Professor in Remote Sensing and Deep Learning of woody ecosystem properties

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 and Assistant Professor position in Remote Sensing and Deep Learning of woody ecosystem properties. The position 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 Assistant Professor 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 research 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 15 September 2026 or as soon as possible thereafter and will be for a duration of 15 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.

Assessment of applicants will primarily consider their level of documented, internationally competitive research. The ability to attract external funding will be considered together with outreach qualifications. Teaching qualifications are not mandatory but documented teaching qualifications and teaching experience will be considered.

Six overall criteria apply for assistant professor appointments at the University of Copenhagen . The six criteria (research, teaching, societal impact, organisational contribution, external funding and leadership) are considered a framework for the overall assessment of candidates. Furthermore, each candidate must be assessed according to the specific requirements stated in the job advertisement.
Please read more at .

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 . 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 position as assistant projessor is 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

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

The deadline for applications is 19 July, 23:59 GMT +2 .                                                                               

After the expiry date of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee.

You can read about the recruitment process at .