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Artificial Intelligence Research Institute

Advancing research, innovation, and exploration in the field of artificial intelligence at the Technical University of Cluj-Napoca.

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AIRi@UTCN is currently under construction. Follow our channels for updates.

About Us

The Artificial Intelligence Research Institute (AIRi) is a nexus for collaborative research at the Technical University of Cluj-Napoca. AIRi@UTCN promotes excellence in AI theory and practice, bringing together researchers across UTCN around a vision of open collaboration. Our work spans interdisciplinary research, AI literacy across disciplines, and impact through business and public co-creation partnerships.

Committee on European Affairs @ AIRi@UTCN

Committee on European Affairs @ AIRi@UTCN

AIRi@UTCN was pleased to welcome the delegation of the Committee on European Affairs of the Chamber of Deputies, led by Deputy Ovidiu-Vasile Cîmpean, Vice-Chair of the Committee, during its working visit to Cluj-Napoca, held in the context of the public debate on the future EU budget for 2028–2034.

Center of Immersive Technologies for Education

Center of Immersive Technologies for Education

AIRi is happy to announce the completion of this milestone: UTCN's Center of Immersive Technologies for Education, funded through eUT4ALL (e-PNRR: 1277457265) and hosted by AIRi, is now up and running! More updates to come! https://lnkd.in/d7_-yt4Y

Invited Lecturer - Alina Donea, Monash University, Australia

Invited Lecturer - Alina Donea, Monash University, Australia

Solar physics offers a uniquely rich and multi-scale environment for data science, combining high-resolution imaging, time-series analysis, and complex physical modelling. Modern missions, such as Solar Dynamics Observatory, continuously produce terabytes of data per day, including multi-wavelength images, Doppler velocity fields, and magnetic maps of the Sun. From a data science perspective, solar datasets are inherently high-dimensional, spatio-temporal, and physics-constrained, making them ideal for machine learning applications such as convolutional neural networks for image recognition, recurrent or transformer models for time-series forecasting, and unsupervised methods for pattern discovery. At the same time, they challenge purely data-driven approaches, as physical interpretability remains essential—for example, linking learned features to magnetic structures, energy transport, or wave dynamics.