Climate AI Nordics Newsletter
Welcome to the March edition of the Climate AI Nordics Newsletter!
Since launching in October 2024, our community has grown to 231 members. Together, we connect researchers and practitioners working at the intersection of artificial intelligence and climate action, spanning mitigation, adaptation, and environmental monitoring.
If you know colleagues in academia, public agencies, or industry who share these interests, invite them to join us at climateainordics.com/join.
This month’s issue features community updates, job opportunities, and our featured member.
Community spotlight
News
No current news.
Coming events
Machine learning based classification of tree crops of Syrian Arab Republic
Event date: 2026-04-23.
Webinar with Purnendu Sardar, Lund University. Accurate mapping of tree crops is vital for regional resource management, ecosystem service assessment, and the support of local livelihoods within the Syrian Arab Republic. Despite their socio-economic importance, tree crops are frequently misclassified or omitted in global and regional cropland products due to their complex spectral signatures and structural similarities to natural vegetation. This study proposes an integrated machine learning framework that combines the computational power of Google Earth Engine (GEE) with Python to enhance classification precision of tree crops across Syria. The methodology evaluates the integration of Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with Sentinel-2 multi-spectral imagery to facilitate robust tree crop mapping. By utilizing GEE for the large-scale preprocessing of Sentinel-2 time-series data, the workflow generates high-dimensional, machine-learning-ready datasets that incorporate both structural and phenological variables. A Convolutional Neural Network (CNN) is subsequently trained in Python, chosen for its proficiency in processing time-series remote sensing data where temporal spectral patterns are more diagnostic than spatial textures. This approach allows the model to capture the distinct phenological cycles of various tree species, overcoming the limitations of traditional pixel-based or purely spatial classifiers. The findings underscore the efficacy of the CNN in distinguishing tree crop cover with high efficiency, demonstrating that the fusion of LiDAR-derived structural metrics with multi-temporal satellite data significantly reduces classification errors. The resulting high-resolution tree crop map provides an essential tool for sustainable agricultural planning and resource allocation in Syria.
Read more!
Swedish Climate Symposium
Event date: 2026-05-20.
The call for abstracts is now open for the Swedish Climate Symposium (Lund, May 20–22, 2026). The event focuses on “Science, Society, and Actions,” connecting researchers with policymakers. If you are applying AI or data science to climate challenges, don’t miss the chance to present your findings. Registration opens in January.
Read more!
Climes interdisciplinary summer school 2026
Event date: 2026-06-15.
The Climes Summer School 2026 at Uppsala University offers doctoral, postdoc, and advanced master’s students an interdisciplinary curriculum focused on climate extremes, public health, and societal impacts. The program features a specialized AI component where participants use deep learning and natural language processing to automate the extraction of climate data from texts. While the school is free to attend, applicants must submit their motivation and support letters by March 22nd, 2026, and are generally responsible for their own travel and lodging.
Read more!
2026 Nordic Workshop on AI for Climate
Event date: 2026-06-26.
The 2026 Nordic Workshop on AI for Climate will gather researchers from the Nordics. This one-day, in-person workshop, will take place in Copenhagen, June 26th 2026. The workshop will feature a mix of keynotes, oral presentations, and posters around the topics of AI for tackling climate change, including AI for biodiversity and the green transition. The workshop will be a meeting point for a wide range of researchers from (primarily) around the Nordic countries.
Read more!
Job openings
Research Scientist (Postdoc) - Remote Sensing
Natural Resources Institute Finland (Luke) is hiring a postdoctoral researcher for the “Digital Twin of Boreal Forest Structure” project, focusing on developing machine learning methods for large-scale, tree-level forest mapping using remote sensing data for biodiversity.
Deadline: April 22nd, 2026
PhD Student for Sustainable and Resource-Efficient Machine Learning
Linköping University is seeking a PhD student to research sustainable and resource-efficient machine learning, focusing on methodologies to reduce computational, energy, and storage demands, and associated carbon emissions, while maintaining model quality.
Deadline: April 24th, 2026
PhD Fellowship in Machine Learning for Wetland-Based Climate Change Mitigation
The University of Copenhagen is offering a fully-funded PhD fellowship at its Department of Computer Science, focusing on developing novel machine learning methods to model greenhouse gas fluxes from wetlands using remote sensing and ground-level measurements for climate change mitigation.
Deadline: April 06th, 2026
Associate Senior Lecturer (Assistant Professor) in Data-Driven Evolution and Biodiversity
The Swedish University of Agricultural Sciences (SLU) is recruiting a tenure-track fellow to develop data-driven research in evolution and biodiversity, applying machine learning and computational methods to aquatic or terrestrial systems.
Deadline: 2026-04-17
Machine learning engineer for computer vision and controlled pesticide-use.
Dimensions Agri Technologies (DAT) are recruiting a machine learning engineer. The selected candidate will help reduce and optimize the use of pesticides by developing targeted schemes through machine learning model assisted computer vision.
Deadline: Rolling
Your news in the newsletter!
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Climate AI Nordics is a network of researchers working to harness AI in tackling the climate crisis through both mitigation and adaptation.
We promote the development of AI-based tools and optimization methods that support sustainable decision-making—helping reduce emissions, restore ecosystems, and build climate resilience.