Climate AI Nordics Newsletter
Welcome to the April edition of the Climate AI Nordics Newsletter!
Since launching in October 2024, our community has grown to 235 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
Upcoming seminars spring/summer 2026
2026-04-17 The Spring/Summer 2026 Climate AI Nordics webinar series features an elite lineup of experts exploring the intersection of artificial intelligence and climate science. The program covers diverse topics ranging from satellite-based crop mapping and flood detection to the emergence of social conventions in AI populations. This effort brings together prestigious speakers from institutions like Lund, Oxford, and the Max-Planck-Institute to share cutting-edge research with a global audience. These sessions provide a vital platform for researchers and policymakers to engage in innovative solutions for navigating the world’s most pressing climate challenges.
Read more!
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!
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
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!
Make sure to share your work with us, by sending us an email (contact@climateainordics.com), posting in our Slack or some other channel, and we’ll add it to the news feed! Take the chance of showcasing your work or your events to the community!
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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.