Low-Carbon ML for Climate-Smart Forests & Wetlands
Climate AI Nordics
Natural Resources Institute Finland (Luke) Postdoctoral researcher for the "Digital Twin of Boreal Forest Structure" project, focusing on developing machine learning methods for large-scale, tree-level forest mapping using remote sensing data for biodiversity. Linköping University PhD student researching sustainable and resource-efficient machine learning, focusing on methodologies to reduce computational, energy, and storage demands, and associated carbon emissions, while maintaining model quality. University of Copenhagen Fully‑funded PhD fellowship at its Department of Computer Science, focusing on developing novel machine learning methods to model greenhouse gas fluxes from wetlands using remote sensing and ground‑level measurements for climate change mitigation. #J-18808-Ljbffr
Vacancy posted 1 day ago
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