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PhD in AI-Driven Circular Material Recovery for Energy Applications
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RISE Research Institutes of Sweden is recruiting a PhD student to develop AI-driven approaches for recovering and reusing composite materials for energy applications, in collaboration with Swedish universities and industrial partners.
Doctoral student in Predicting Hydrological Extremes with Earth Data
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KTH Royal Institute of Technology is recruiting a PhD student to develop Earth observation–based indicators and machine learning models to improve forecasting of consecutive drought-to-flood events.
Associate Senior Lecturer (Assistant Professor) in Data-Driven Evolution and Biodiversity
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The Swedish University of Agricultural Sciences (SLU) is recruiting a tenure-track fellow to develop data-driven research in evolution and biodiversity, applying machine learning and computational methods to aquatic or terrestrial systems.
Bridging data gaps in the Global South: Harnessing AI and Earth observation in the humanitarian sector
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Webinar with Isabelle Tingzon, RISE. This talk highlights how AI and Earth Observation (EO) are transforming data availability for two global challenges: climate resilience and universal school connectivity. In the Caribbean, where extreme climatic hazards like hurricanes, floods, and landslides can devastate over 90% of built infrastructure across Small Island Developing States, comprehensive and up-to-date housing stock information is critical to enable resilient housing operations but is often incomplete, inaccessible, or completely non-existent. The “Digital Earth for Resilient Caribbean” initiative addresses these gaps by integrating EO data such as drone, LiDAR, satellite, and street-view imagery with AI workflows to classify roof types, materials, and building conditions. These efforts aim not only to enhance disaster response and recovery but also strengthen long-term resilience through government capacity building and knowledge exchange. In parallel, the presentation showcases UNICEF Giga’s mission to connect every school to the Internet by 2030. Reliable school location data is critical for planning telecommunications infrastructure, yet often inaccurate or incomplete in many low- and middle-income countries. By leveraging ML and satellite imagery, we’ve developed scalable pipelines to map schools across Africa on a nationwide scale, helping to ensure that no child is left behind in accessing digital learning opportunities. Together, these projects demonstrate how Geospatial AI can help bridge critical data gaps in the Global South, supporting both climate adaptation and inclusive education.