Climate AI Nordics Newsletter
Welcome to the May edition of the Climate AI Nordics Newsletter!
Since launching in October 2024, our community has grown to 240 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
New Strategic Partnership: Climate AI Nordics and Climate Change AI
2026-05-08 Climate AI Nordics (CAIN) is proud to announce a formal partnership with Climate Change AI (CCAI). This collaboration aims to bridge the gap between AI expertise and climate action by facilitating the exchange of knowledge, resources, and joint initiatives between the Nordic region and the global community. By aligning our missions, we seek to accelerate responsible AI-driven solutions for climate mitigation and adaptation.
Read more!
Coming events
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!
More is different: emergent social conventions and tipping points in AI populations
Event date: 2026-06-04.
Webinar with Ariel Flint Ashery, City St George’s, University of London. Social conventions are the foundation of social coordination, shaping how individuals come together to form a society. In this talk, I will present theoretical and experimental findings that demonstrate the spontaneous emergence of social norms in LLM populations, as well as the existence of tipping points in social convention. I will show that agentic AI populations can establish social conventions and highlight how collective biases can emerge even when individual agents appear unbiased. I will conclude by stressing how the ability of AI agents to develop norms without explicit programming has significant implications for designing AI systems that align with human values and societal goals.
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!
HydroImaging: Mining Imaging Data for Hydrological and Environmental Modelling
Event date: 2026-09-13.
Submit your research to HydroImaging, a half-day IEEE ICIP 2026 workshop in Tampere, Finland (September 13–17, 2026). This workshop bridges computer vision, remote sensing, and environmental science to address climate change and the water cycle. Contributions on data-centric ML, multi-modal fusion, and disaster mapping are welcome.
Read more!
Recent events
Enhanced Flood Detection through Innovative integration of PolSAR, metaheuristic optimization, and deep learning-based segmentation
This event took place 2026-05-07. Webinar with Solmaz Khazaei, KTH Royal Institute of Technology. Flood is the most common natural disaster in the world, and can have catastrophic impacts on human society and the environment, including infrastructure damage, agricultural losses, and casualties, resulting in widespread economic and social disruptions. In early studies, water body detection relied on on-the-spot investigation, hydrological models and common remote sensing techniques that face issues like slow processing and real-time delays. By addressing this challenges we propose a novel hybrid PoLSAR-metaheuristic-DL models and high-resolution remote sensing data to generate accurate and rapid flood mapping for one of the huge recent flood in France. Compared with standard synthetic aperture radars (SAR), polarimetric synthetic aperture radar (PolSAR) is an advanced technique of SAR remote sensing. So, by using polarimetric decomposition methods, features were extracted and feature selection problem, one of the most challenging, was solved by using metaheuristic techniques. The selected features fed into three deep learning-based segmentation models- U_Net_V3, Nested_UNet and Efficient_UNet. The reliability of the generated flood maps was evaluated using Accuracy, precision and recall metrics. Our experimental results indicate that Nested_UNet integrate with optimized PolSAR data achieves the highest segmentation performance, with an accuracy of 0.910, precision of 0.914, and recall of 0.909. These findings underscore the capability of Nested_UNet, demonstrates superior feature extraction abilities, making it a promising choice for real-time flood segmentation applications. Moreover, detecting the knowledge of flooded areas, officials can actively adopt steps to reduce the potential impact of flood, ensure the sustainable management of natural resources and mitigate flood impacts.
Job openings
Doctoral position in new atmospheric satellite missions
Chalmers University of Technology is hiring a Doctoral Student for bridging instrument design and space-based environmental observations using physics and machine learning.
Deadline: 2026-05-15
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.