Climate AI Nordics Newsletter
Welcome to the November edition of the Climate AI Nordics Newsletter!
Enjoy our community highlights, updates from our partners, events, and new job opportunities!
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.
Community spotlight
News
No current news.
Coming events
AtmoSTEM: From high-resolution emission mapping to environmental intelligence
Event date: 2026-11-05.
Webinar with Anastasia Kakouri, University of Aegean. The spatial and temporal representation of anthropogenic emissions is essential for atmospheric modelling, yet its influence on the predictive performance of data-driven air quality models remains insufficiently assessed. This study introduces AtmoSTEM, a modular European framework integrating high-resolution emission modelling with machine learning-based concentration prediction. AtmoSTEM combines CAMS-REG emission inventories, sector-specific geospatial proxies, and CAMS-TEMPO profiles to generate spatially and temporally resolved anthropogenic emissions. The initial application covers daily PM2.5 and PM10 emissions at 1 km spatial resolution across Europe for 2015–2024. The resulting emissions are integrated with meteorological, satellite-derived, atmospheric composition, and geospatial information to support machine learning-based predictions of ground-level pollutant concentrations. An Extreme Gradient Boosting (XGBoost) approach is employed to investigate the relationships between anthropogenic emissions, environmental conditions, and observed air pollution levels, with particular emphasis on the contribution of high-resolution emission information to predictive performance. Model predictions are evaluated against independent ground-based observations from air quality monitoring stations across Europe. The study aims to assess the potential of integrating high-resolution spatiotemporal emissions with machine learning for air quality prediction, providing a basis for more spatially detailed population exposure assessments and epidemiological research.
Read more!
Learning when and where to adapt: Reinforcement learning for climate-resilient urban transport
Event date: 2026-12-03.
Webinar with Miguel Costa, Technical University of Denmark. Cities need to decide now which climate adaptation measures to build, where to put them, and when, while facing deep uncertainty about how the climate will change over the coming decades. Each investment will last for a few decades and will be costly. Their impacts and benefits can also play out through coupled systems, such as, rainfall drives flooding, flooding disrupts how people move around the city, creating economic and wellbeing impacts. Classical optimisation methods struggle at the city-scale over a planning horizon of 75 years or more. In this talk, I will present a decision-support framework which can address this problem by embedding an integrated assessment model inside a reinforcement learning (RL) loop. The model chains climate projections, a flood model, a transport simulation and an assessment of direct and indirect impacts. A graph neural network policy learns adaptation pathways from this model: it decides per zone and per year which nature-based and grey measures to deploy, balancing investment and maintenance costs against avoided damage, travel delays and cancelled trips. On a full-city case study of pluvial flooding in Copenhagen (278 zones, 2025–2100, 42 scenarios from EURO-CORDEX), RL cuts total costs by about 25% compared with doing nothing and outperforms competing alternatives across two case studies (Bayesian optimisation, CMA-ES, trigger rules, Copenhagen’s own Cloudburst Plan design standard, and Direct Policy Search). Results hold across scenarios, including on held-out climate trajectories. I will then discuss how such a framework can support policymakers in making informed decisions and ongoing extensions of this work and opportunities for broader application, including optimization for non-economic impacts (e.g., quality of life, wellbeing), other case studies, and climate impacts.
Read more!
3rd Nordic Workshop on AI for Climate (2027)
Event date: 2027-04-22.
The 2027 Nordic Workshop on AI for Climate will gather researchers from the Nordics. This one-day, in-person workshop will take place in Oslo, April 22nd 2027. 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
Postdoctoral Researcher, Machine Learning & Cheminformatics
VTT is seeking a Postdoctoral Researcher, Machine Learning & Cheminformatics to join their Q-GEN project, focusing on quantum computing applications for future genomes and biomaterials.
Deadline: November 8th, 2026
Master’s thesis: Robust and uncertainty-aware urban land-cover mapping at property scale
A master’s student is sought to work on a real-world research challenge at IVL Svenska Miljöinstitutet, focusing on urban land-cover mapping using AI, aerial imagery, and GIS to support climate and biodiversity planning.
Deadline: November 30th, 2026
Doctoral Researcher (PhD Student), 3 positions, Development of Data Science and AI for Spatial Data
The University of Eastern Finland is seeking three Doctoral Researchers (PhD Students) to join a project focused on the development of Data Science and AI for Spatial Data, with an emphasis on mapping mining activities and their global impacts.
Deadline: November 17th, 2026
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.