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Reflections on the first EurIPS and a successful AICC workshop
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Climate AI Nordics joined the inaugural EurIPS conference in Copenhagen last week, connecting with the community at our booth shared with the Nordic AI Partnership and Pioneer Centre for AI. On Saturday, we successfully hosted the full-day AICC workshop, featuring an inspiring lineup of speakers and over 30 posters bridging machine learning and climate science. While the research highlights covered diverse topics like weather forecasting, a significant portion of the program focused on analyzing forests as vital carbon sinks and biodiversity havens. We are grateful to our partners and all amazing researchers from around Europe and the rest of the world for supporting this memorable week of collaboration, glögg, and impactful science.
Reflections on the first EurIPS and a successful AICC workshop
Published:
Climate AI Nordics joined the inaugural EurIPS conference in Copenhagen last week, connecting with the community at our booth shared with the Nordic AI Partnership and Pioneer Centre for AI. On Saturday, we successfully hosted the full-day AICC workshop, featuring an inspiring lineup of speakers and over 30 posters bridging machine learning and climate science. While the research highlights covered diverse topics like weather forecasting, a significant portion of the program focused on analyzing forests as vital carbon sinks and biodiversity havens. We are grateful to our partners and all amazing researchers from around Europe and the rest of the world for supporting this memorable week of collaboration, glögg, and impactful science.
PhD position: Determining the utility of newly available hyperspectral satellite data to better understand and quantify biochemical and biophysical properties of tundra vegetation negatively impacted by changing climatic conditions.
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University of Gothenburg is hiring a PhD student in Natural Sciences, specializing in Physical Geography. Project funded mainly be the Swedish National Space Agency.
Featured member, December 2025: Laura Ruotsalainen
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Laura Ruotsalainen is a Professor of Computer Science at the University of Helsinki and leads the Spatiotemporal Data Analysis research group. Her work focuses on developing machine learning methods for spatiotemporal data, particularly in reinforcement learning, representation learning, and uncertainty-aware models, motivated by challenges in climate, sustainability, and urban resilience. She also serves as Vice-Chair of the ELLIS Institute Finland and is a steering group member of the Finnish Center for Artificial Intelligence (FCAI)