Climate AI Nordics

Climate AI Nordics

Nordic network for collaborations around tackling climate change using AI and machine learning.

AI/UI full stack engineer

Unibloom is on a mission to transform how procurement collaborates faster and smarter to achieve both climate and financial goals. Their platform connects emissions, cost, and supply chain data, automating scenario calculations to help procurement and innovation teams identify and prioritize sustainability initiatives.

They are looking for a hands-on, AI-native developer with strong TypeScript experience who loves turning complex data and calculations into intuitive, decision-ready interfaces. You will join a small, fast-moving team of engineers, data scientists, and sustainability experts.

Apply by email: anna.sandgren@unibloom.world

You May Also Enjoy

Climate AI Nordics launches official YouTube channel

Published:

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.

Featured paper: Separating the Albedo-Reducing Effect of Snow Impurities Using Machine Learning

Published:

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.

Featured member, November 2025: Joakim Bruslund Haurum

Published:

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.