PostDoc in Remote Sensing and Deep Learning of woody ecosystem properties

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.

Responsibilities

• 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.

Requirements

• Hold a PhD degree in Geography, Geoinformatics, Environmental Sciences, or related fields.
• Good interpersonal and communication skills.
• Fluency in spoken and written English.
• Previous publications, relevant experience in remote sensing and forest monitoring, and programming skills (e.g., R, Python).
• Proven experience with high-resolution imagery and machine/deep learning techniques.
• Proven experiences with handling and processing large image datasets.

Qualifications

• PhD degree in Geography, Geoinformatics, Environmental Sciences, or related fields.
• Strong interpersonal and communication skills.
• Excellent command of English.
• Relevant research experience in remote sensing and forest monitoring.
• Programming skills (e.g., R, Python).
• Experience with high-resolution imagery and machine/deep learning techniques.
• Experience with handling and processing large image datasets.

Skills

• Advanced knowledge of remote sensing technologies and AI algorithms.
• Experience with deep learning architectures, including convolutional neural networks, vision transformers, and foundation models.
• Ability to integrate multi-sensor data to resolve challenging species mixtures and structural variability.
• Experience with weak supervision techniques and existing species maps, national forest inventory data, and targeted field plots.
• Experience with designing robust training pipelines for model generalization across biogeographical regions and sensor types.
• Experience with open, reproducible workflows and uncertainty quantification.

Benefits

• Independent, observation-based constraints on forest carbon dynamics relevant for climate mitigation policies, greenhouse gas reporting, and biodiversity conservation at the European level.
• New insights into species-specific risk profiles under climate change through linking species-distribution patterns with observed pest outbreaks and drought events.
• Core layer for assessing forest vulnerability and resilience.

Pay

• Salary is determined according 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.

Schedule

• The first position is open from October 15th 2026 or as soon as possible thereafter and will be for a duration of 36 months.
• The second position is open from September 1st 2026 or as soon as possible thereafter and will be for a duration of 18 months.