"Success in the GA ČR grant procedure strengthens the international prestige of our faculty and provides essential support for the development of research teams in the fields of computer science, robotics, and artificial intelligence. I am proud of our scientists, who are succeeding in a demanding competitive environment and helping to build the reputation of FEE CTU as a leading research institution in the country," said Prof. Petr Páta, Dean of FEE CTU, commenting on the award.
The faculty's dominant position in technical sciences is no coincidence—it is the result of long-term efforts to achieve excellence in research, international cooperation, and systematic support for fields that shape modern technological society.
The Grant Agency of the Czech Republic (GA ČR), as the most important provider of support for basic research projects in the Czech Republic, will begin financing over 400 scientific projects from all areas of basic research starting next year. In total, they will receive over CZK 3.7 billion.
Standard GA ČR projects awarded to FEE CTU scientists since 2026
Doc. Dr. rer. nat. Martin Saska: Sensing abstract behavioral patterns to allow coordinated fast response to disruptions in multi-robot systems
Ing. Vojtěch Franc, Ph.D.: Uncertainty-Aware Machine Learning Models for Open-World Decision-Making
Ing. Gustav Šír, Ph.D.: Neuro-Symbolic Learning for Relational Databases
Ing. Vojtěch Vonásek, Ph.D.: Sampling methods for motion planning and control using learned spaces
Giulia D’Angelo, Ph.D.: Neuromorphic active vision for embodied object perception (PIONEER)
Prof. Ing. Jan Kybic, Dr.: Leveraging expert knowledge for medical image segmentation
Prof. Ing. Jiří Bittner, Ph.D.: Efficient Spatial Hierarchies for Complex 3D Scenes
Ing. Ondřej Kuželka, Ph.D.: Probing Deep Learning Models by Logic
Doc. Georgios Tolias, Ph.D.: Instance-level Visual Recognition and Generation
Postdoc Individual Fellowship – Outgoing
MSc. Prashant Dwivedi, Ph.D.: High-Velocity Dust Impacts on Tungsten Plasma-Facing Materials: A Predictive Multi-Scale Modeling Framework with Experimental Validation
