PhD fellowships in Remote Sensing and Deep Learning understanding woody ecosystem response

Job Title

PhD fellowships in Remote Sensing and Deep Learning for understanding woody ecosystem response to climate change.

The Department of Geosciences and Natural Resource Management invites applicants for two PhD fellowships in Remote Sensing and Deep Learning for understanding woody ecosystem response to climate change. The PhD positions are part of the Center for Remote Sensing and Deep Learning of Global Tree Resources funded by the Danish National Research Foundation. The center focuses on the critical role of trees in terrestrial ecosystems, such as climate regulation, biodiversity support, and local livelihoods.

Earliest start date is 1 June 2026, but later start dates are possible.

The research center aims to revolutionize global tree monitoring using advanced nano‑satellite technology and next‑generation deep learning methods within AI. This approach will enable detailed assessment of global tree dynamics, including key functional and structural properties such as species, tree use, horizontal and vertical structure, carbon stocks and sequestration rates. The research addresses major unknowns in global change research, quantifies the effects of global warming and extreme events on tree physiology and growth patterns at species level, and measures anthropogenic forest disturbance and degradation.

Ultimately, it will uncover potentials for forest and tree‑related production systems and human livelihoods as climate mitigation actions while improving our understanding of woody resources for sustainable food systems.

The PhD scholarships will generate annual global high‑resolution satellite‑based datasets assessing a wide range of woody properties at the level of single trees and quantified at tree‑ or patch‑level (e.g. tree count, forest structure complexity, biomass or species diversity). Scholarship One focuses primarily on developing deep learning technical methods for improved characterisation and understanding of global woody ecosystem changes. Scholarship Two focuses more on the applied dimension relating remotely sensed information to climate change, ecology and geoscience aspects.

Applicants should indicate their preference for one or two (or show equal interest) in their proposal.

The PhD students will develop research techniques for improved characterisation of changes in global woody ecosystem functioning and structure and for better understanding of woody ecosystem responses to global environmental change. This includes characterising selected tree species distributions at species or genus level, leveraging new methods to assess resilience and vulnerability to climate change impacts. Output predictions will be based on existing networks, ground observations, sub‑meter resolution satellite training data or coarse resolution labels of existing satellite‑based products.

The scholarships include an international research exchange stay and the potential for conducting fieldwork in relevant case areas with partners LSCE (France) and CREAF (Spain) and several Chinese universities.

• Associate Prof. Martin Brandt (scholarship One) – ma .dk
• Prof. Rasmus Fensholt (scholarship Two) – rf.dk
• Co‑PIs:
  Assistant Prof. Ankit Kariryaa and Prof. Christian Igel, Department of Geoscience and Natural Resource Management / Department of Computer Science
• International Center Co‑PIs:
  Philippe Ciais (LSCE France) & Josep Peñuelas (CREAF Spain)

• Highly motivated scholars with good interpersonal and communication skills.
• Fluency in spoken and written English is a requirement.
• Python programming skills and relevant experience in remote sensing and deep learning.
• Experience with handling and processing large image datasets and scientific publications.
• Prior experience with remote sensing of tree resources is an advantage, but not a formal requirement.
• Curious mind‑set with a strong interest in how remote sensing and AI can assist in reaching the global sustainable development goals (SDG 13 Climate Action, SDG 15 Life on Land).
• Good language skills.

• Carry through an independent research…