Climate AI Nordics Newsletter, November 2025
Welcome to the November edition of the Climate AI Nordics Newsletter!
This month, we are celebrating a major milestone that highlights the true power of this community. Our partner Klimatkollen has secured 3 million SEK from the Postcode Lottery Foundation for a project on AI-driven climate plan analysis—a collaboration with NORCE that was formed explicitly through connections made right here in this network.
In addition to this success story, we are continuing our one-year anniversary from last month with the launch of our official YouTube channel, giving you open access to our full archive of workshops and webinars.
As our network grows to 199 members across the Nordics and 72 international supporters, we look forward to seeing many of you in Copenhagen next month for EurIPS 2025. Until then, explore our latest updates, job opportunities, and featured research below.
If you know colleagues in academia, public agencies, or industry who should be part of this conversation, please invite them to join us at climateainordics.com/join.
News
Community win: Klimatkollen secures 3 MSEK to analyze climate plans with AI
2025-11-28 Great news from our community! Through partnerships found right here at Climate AI Nordics, Klimatkollen has received 3 million SEK from the Swedish Postcode Lottery Foundation. They are teaming up with NORCE to use the AI model “Garbo AI” to analyze municipal climate plans. This project will increase transparency and help standardize local climate data across Sweden.
Read more!
Climate AI Nordics launches official YouTube channel
2025-11-25 To celebrate our one-year anniversary, Climate AI Nordics is launching a new YouTube channel dedicated to sharing knowledge within the AI and climate community. The channel features a complete set of recordings from the inaugural Nordic Workshop on AI for Climate Change held in Gothenburg this past May. In addition to workshop content, viewers can access an extensive library of past webinars covering topics such as biodiversity monitoring, weather forecasting, and Earth system modelling. We invite researchers and practitioners to explore these valuable resources and join us as we look forward to another year of collaboration and innovation.
Read more!
Climate AI Nordics at EurIPS 2025
2025-11-24 Here we summarize Climate AI Nordics-related activities at EurIPS 2025!
Read more!
Featured work
Featured paper: Separating the Albedo-Reducing Effect of Snow Impurities Using Machine Learning
2025-11-24 This study presents an AI-driven inversion framework that uses a deep-learning emulator of a radiative transfer model to estimate snow albedo and quantify the impact of light-absorbing particles (LAPs) such as dust, black carbon, and algae. Trained on 5.8 million simulations, the method accelerates computations by 30× while maintaining accuracy, enabling large-scale mapping from drone or satellite data. Applied to field observations, it revealed strong but variable LAP effects on albedo and complex interactions between multiple impurities, offering a powerful tool for improving melt forecasts and climate projections.
Read more!
Featured member
Featured member, November 2025: Joakim Bruslund Haurum
2025-11-24 Joakim Bruslund Haurum, an Assistant Professor at the University of Southern Denmark’s new Vejle Campus and the Pioneer Centre for AI, is the featured member for November 2025. He is a Computer Vision researcher specializing in expert tasks, balancing pure research on algorithms with applied work in infrastructure inspection and biodiversity monitoring. Beyond his studies on hierarchical data, Joakim engages with the scientific community (and with Climate AI Nordics) by co-organizing events like the AI for Climate and Conservation workshop at EurIPS. He is currently seeking collaborations with others interested in solving challenges related to messy data in expert domains.
Read more!
Coming events
Workshop on AI for Climate and Conservation (AICC) at EurIPS 2025
Event date: 2025-12-06.
Climate AI Nordics is excited to announce that the Workshop on AI for Climate and Conservation (AICC) has been accepted for EurIPS 2025! The AICC workshop will take place in Copenhagen, Denmark, Dec 6th! (Co-located with EurIPS).
Read more!
International Conference: Climate Impacts in a Changing World 2026
Event date: 2026-03-09 to 2026-03-11.
The Swedish Centre for Impacts of Climate Extremes (CLIMES) invites abstract submissions for the international conference Climate Impacts in a Changing World 2026, held in Uppsala on March 9–11, 2026. The event fosters interdisciplinary dialogue on the wide-ranging impacts of climate extremes on human and natural systems.
Read more!
Workshop on Advances in Representation Learning for Earth Observation (REO) at EurIPS 2025
Event date: 2025-12-06.
The REO workshop will take place in Copenhagen, Denmark, Dec 6th. (Co-located with EurIPS)!
Read more!
TESSERA: precomputed FAIR global pixel embeddings for earth representation and analysis
Event date: 2025-12-11.
Webinar with Zhengpeng (Frank) Feng, Cambridge. Petabytes of satellite Earth Observation (EO) data are freely available and can address critical global challenges. However, EO data quality is poor due to clouds and variable lighting conditions. To address this, practitioners typically use compositing, but this critically removes the temporal phenological signal. Moreover, supervised machine learning to map composited pixels to task-specific classes requires accurately labelled data that are rarely available. We present TESSERA, a pixel-oriented foundation model for EO time series that creates 128-dimensional latent embeddings requiring only a few labels for task-specific training to achieve state-of-the-art performance across diverse complex tasks. TESSERA uses two encoders that combine optical data with synthetic aperture radar backscatter coefficients at 10m resolution, creating embeddings fused with a multilayer perceptron to generate annual global embedding maps. TESSERA closely matches or outperforms state-of-the-art task-specific models and other foundation models across five diverse downstream tasks. It is unprecedented in ease of use, scale, and accuracy: no other open foundation model provides precomputed outputs with global, annual coverage at 10m resolution.
Read more!
SBDI Days 2026: Artificial Intelligence in Ecology and Biodiversity Research
Event date: 2026-02-10.
A conference hosted by SBDI in partnership with SciLifeLab’s Planetary Biology Strategic Area, exploring the transformative partnership between AI and biodiversity research.
Read more!
Recent events
Multi-resolution learning with neural operators and long short-term memory neural networks
This event took place 2025-11-20. Webinar with Katarzyna Michalowska, SINTEF. In real-world applications, collecting large amounts of high-resolution data is rarely practical. The cost of specialized equipment, time-intensive measurements, and expensive high-fidelity simulations often means that high-resolution samples are limited. Meanwhile, low-resolution data are far easier to obtain and often exist in abundance, creating a common scenario: plentiful coarse data but limited fine data. Standard neural networks, which require fixed-resolution inputs, cannot exploit this imbalance and typically perform poorly when generalizing across resolutions. Deep operator networks, or DeepONets offer a distinct advantage over standard neural networks through a property known as discretization invariance, enabling learning across varying data resolutions. However, DeepONets alone do not effectively capture long-term temporal dependencies, limiting their performance on problems involving long time horizons. In this talk, we present a framework that addresses both challenges: multi-resolution learning and long-time horizon modelling. We achieve this by extending DeepONets with long short-term memory networks (LSTMs) and introducing a multi-stage training procedure that leverages data at multiple resolutions. This hybrid architecture first learns global dynamics from abundant low-resolution data and then fine-tunes on limited high-resolution samples, capturing both multi-resolution structure and temporal dependencies. In tests on nonlinear dynamical systems, our multi-resolution DON-LSTM achieves lower generalization error and requires fewer high-resolution samples than standard DeepONet or LSTM models. Our results demonstrate that the proposed approach is well-suited for real-world scenarios where high-resolution data are limited, highlighting its potential for practical applications across science and engineering.
Self-supervised pre-training for glacier calving front extraction from synthetic aperture radar imagery
This event took place 2025-11-13. Webinar with Nora Gourmelon, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The factors influencing melt at glacier fronts facing the ocean remain an active area of research. To better understand these factors, it is essential to monitor changes in glacier calving fronts. Due to the importance of weather-dependent and seasonal variations, Synthetic Aperture Radar (SAR) is the preferred imaging method for such monitoring. In recent years, deep learning models have been developed to automate the extraction of calving front positions, however, their performance on SAR data remains suboptimal. Limited labeled data and high variability in SAR images hinder traditional supervised learning. Foundation models pre-trained on large, diverse datasets could provide robust feature representations that require minimal labeled data for fine-tuning on specific SAR tasks. However, in preliminary experiments, we found that the domain gap is too large for the task of extracting calving fronts from SAR imagery, and foundation models fine-tuned on the "CAlving Fronts and where to Find thEm" (CaFFe) benchmark dataset performed subpar. Therefore, we compiled an unlabeled dataset of Sentinel-1 SAR image sequences of Arctic glaciers, each associated with a single Sentinel-2 optical reference image. Using this dataset, we developed a novel multi-modal self-supervised pre-training strategy and applied it to pre-train a hybrid CNN-transformer model. Fine-tuning on the CaFFe benchmark showed that the pre-trained model outperforms its non-pre-trained counterpart, enabling more robust calving front segmentation and demonstrating the potential of data-efficient learning for SAR imagery.
Job openings
Join the Climate Change AI Core Team!
Climate Change AI is looking for new core team members to play leading roles in the organization’s ongoing activities. (Note that this is for the world-spanning organization Climate Change AI. Currently, Climate AI Nordics is not looking for Core team members.)
PhD position: Cutting-edge machine learning for climate modelling
DTU Compute is hiring a PhD student for the IcyAlert project, focusing on machine learning for climate modelling.
Deadline: 2025-12-08
Doctoral student in environmental science
Lund University is looking for a doctoral student to combine remote sensing and AI to monitor grassland management within the BIOSPACE project.
Deadline: 2025-12-10
PhD position in AI for biodiversity monitoring
Aarhus University is offering a PhD position on harnessing AI for biodiversity monitoring with camera trap networks.
Deadline: 2026-01-15
Researcher in coastal environmental hazards
Permanent position at NERSC (Bergen) focusing on marine ecosystem assessment, optical remote sensing, and machine learning.
Deadline: 2025-12-07
Researcher in climate prediction
Permanent position at NERSC (Bergen) to develop the Norwegian Climate Prediction Model using data assimilation and machine learning.
Deadline: 2025-12-07
AI/UI full stack engineer
Unibloom is looking for an AI-native developer to help transform procurement collaboration for sustainability goals.
PhD position in deep learning for glacier monitoring
A fully-funded 4-year PhD position in Germany focused on developing deep learning methods for glacier surface and bedrock detection.
Deadline: 2025-12-07
Junior researcher in machine learning
RISE is hiring a junior researcher in machine learning to develop AI-based tools for creating smarter, more sustainable cities and to help build a greener future.
Deadline: 2025-11-30
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!
Also be sure to follow us on LinkedIn and BlueSky. Climate AI Nordics will have the most impact if you repost and like our stories!
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.