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Join the ELIAS AI-Based Modeling for Energy-Efficient Buildings Challenge to build cutting-edge Machine Learning (ML) methods for modeling
Heating, Ventilation and Air Conditioning (HVAC) systems of real buildings, and optimising their energy efficiency. Work with real-world building data and compete on Kaggle. Open to all -academics, startups, and professionals.

The AI for Trade Global Challenge is an international competition calling on data scientists, economists, and machine learning experts to reimagine the future of global trade forecasting. Participants will develop predictive models to forecast the trade flows of the United States and China for October 2025—specifically, estimating dollar trade values for the top 200 exports and imports (at the HS4 product level) with the top 20 trading partners.

The Atmospheric Machine Learning Emulation Challenge (AMLEC) invites researchers, data scientists, and practitioners in the fields of remote sensing, climate science, and artificial intelligence to contribute solutions to a key computational challenge in atmospheric modelling. Organised by Jorge Vicent Servera, Gustau Camps Valls, Cesar Aybar, and Julio Contreras from the Image and Signal Processing Group (ISP) at the University of Valencia, AMLEC focuses on advancing surrogate modelling and physics-informed AI in the context of Radiative Transfer Models (RTMs).