Climate AI Nordics Newsletter
Welcome to the June edition of the Climate AI Nordics Newsletter!
We connect the Nordic region’s researchers and practitioners at the intersection of AI and climate action. Whether your focus is on emission mitigation, ecosystem resilience, or biodiversity, this community is built to help your work thrive.
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.
2026 Nordic Workshop on AI for Climate
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.
Registration Deadline: June 5th (AoE)
(Read more)
Community spotlight
Featured member, June 2026: Katarzyna Ostapowicz
Katarzyna Ostapowicz is a researcher at the Norwegian Institute for Nature Research (NINA), specializing in environmental data science and Earth observation.
Read more!
News
No current news.
Coming events
Understanding the complex Earth system with Machine Learning and Hybrid Modelling
Event date: 2026-06-25.
Webinar with Markus Reichstein, Max-Planck-Institute for Biogeochemistry and ELLIS Unit Jena. The Earth system is a complex, dynamic and strongly interconnected system, shaped by interactions between climate, ecosystems, biogeochemical cycles and human activities. Rapidly growing streams of satellite, in-situ and experimental observations, together with advances in machine learning, offer new opportunities to detect patterns, infer processes and improve prediction across scales. Yet purely data-driven approaches often lack physical consistency and interpretability, while classical process-based models remain limited by uncertain parameterizations and incomplete representations of complex feedbacks. In this talk I will discuss how machine learning and hybrid modelling can help bridge this gap. By combining the versatility of data-driven methods with the constraints and explanatory power of mechanistic understanding, hybrid approaches can support more robust, interpretable and physically consistent models of the Earth system. Examples from the terrestrial biosphere, land-atmosphere exchange, carbon and water cycles, and climate extremes will illustrate how such approaches can contribute not only to improved prediction, but also to deeper scientific understanding of Earth system dynamics.
Read more!
More is different: emergent social conventions and tipping points in AI populations
Event date: 2026-08-27.
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!
2nd Workshop on AI for Climate and Conservation (AICC-2) at ECCV 2026
Event date: 2026-09-08.
Climate AI Nordics is glad to announce that the 2nd Workshop on AI for Climate and Conservation (AICC-) has been accepted at ECCV 2026! The AICC-2 workshop will take place in Malmö, Sweden, Sep 8th or 9th (TBD).
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
Climes interdisciplinary summer school 2026
This event took place 2026-06-152026-06-18. 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.
A Critical Look at Explainable AI
This event took place 2026-06-11. Webinar with Gustau Camps-Valls, University of Valencia. I will give a sarcastic and quite op-ed tour trying to explain why XAI is misleading us. Everything from SHAP plots to counterfactuals may look trustworthy, but underneath, they're often driven by correlations, not causation. In fields like climate, neuroscience and social sciences, that's a serious risk. Inspired by philosophy of science, I argue that explanations must go beyond surface patterns. Fortunately, the frontier is moving fast: causal‐informed SHAP, meaningful counterfactuals (you can't go younger), causal certification in explanations, and structural causal modeling are all promising. Yet, it's time we treat XAI not just as a cosmetic fix, but as a tool grounded in truth: seamful, thought-provoking, and scientifically defensible. And if time allows I'd like to say a few words about why AI needs a new philosophy of science.
More is different: emergent social conventions and tipping points in AI populations
This event took place 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.
Job openings
Postdoc in Production Management: AI-driven Sustainability and Resilience
KTH Royal Institute of Technology is seeking a postdoctoral researcher in Production Management to investigate how AI and machine learning can support sustainable and resilient production systems, including circular manufacturing and green industrial transition.
Deadline: June 17th, 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!
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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.