Convened by the African Union and the Ethiopian Government, the Second Africa Climate Summit will provide a platform for policymakers, practitioners, businesses and civil society to advance Africa’s climate agenda and sustainable development priorities. IIASA Strategic Initiatives Program Director Steffen Fritz, will contribute his expertise to discussions at the event as part of a session titled, Leveraging AI to Build Climate Resilience and Adaptive Capacity.
Artificial Intelligence (AI) and Machine Learning (ML) methods are becoming increasingly important in both science and society. In climate science - where complex biophysical and societal processes interact across diverse temporal and spatial scales, and datasets are often large, heterogenous and incomplete - AI and ML methods offer new powerful solutions.
IIASA Director General Hans Joachim (John) Schellnhuber will take part in the next Ö1 Publikumsgespräch in Vienna. On Thursday, 18 September 2025, he will join architect Markus Zilker for a public conversation on the theme, Sustainable building: using wood as a building material, moderated by Ö1 science editor Juliane Nagiller.
Artificial Intelligence (AI) and Machine Learning (ML) methods are becoming increasingly important in both science and society. In climate science - where complex biophysical and societal processes interact across diverse temporal and spatial scales, and datasets are often large, heterogenous and incomplete - AI and ML methods offer new powerful solutions.
Celebrating 80 years, the European Forum Alpbach brings together young leaders, innovators, and experts from around the world to drive ideas for a strong and democratic Europe. This year's theme is "Recharging Europe" - IIASA is proud to join as a reporting partner to share insights on science, innovation, and climate action.
Contemporary socio-ecological-technological systems are non-ergodic: their future trajectories aren’t just probabilistic echoes of the past but are continuously reshaped by evolving causal structures, emergent feedbacks, and path-dependent thresholds.
Risk science thus faces epistemological challenges that can contribute to spawning maladaptive policies, unforeseen trade-offs, and cascading failures.
Artificial Intelligence (AI) and Machine Learning (ML) methods are becoming increasingly important in both science and society. In climate science - where complex biophysical and societal processes interact across diverse temporal and spatial scales, and datasets are often large, heterogenous and incomplete - AI and ML methods offer new powerful solutions.