CVC at Memenginy 2026

CVC participated on April 26 in MEMEnginy 2026, the technology and engineering fair organised by the Engineering Student Council with the support of the management of the School of Engineering at the Universitat Autònoma de Barcelona. This annual event has increasingly established itself as the key meeting point for young talent in Vallès.

MEMEnginy is a university technology fair created to connect young talent with companies and institutions in the ICT and engineering sectors. Organised by the Student Council with the support of the School of Engineering and the UAB, the fair features exhibition spaces, job interviews, talks, and networking activities. This year’s edition welcomed more than 1,500 participants, who had the opportunity to interact with around 80 companies. In parallel, over 1,000 scheduled speed job interviews were conducted throughout the day.

CVC participated with its booth, where it showcased the various opportunities the centre offers to students interested in computer vision and applied research. Throughout the day, the internship programme was presented, designed to allow students to carry out training placements at the centre and actively collaborate in research projects. Information was also provided about the Master’s Degree in Computer Vision, offered jointly by the CVC, UAB, UB, UOC, UPC, and UPF. Attendees were also invited to join the Open Day on May 7, an opportunity to learn about the centre’s research lines, talk with researchers, and discover what it’s like to work at the CVC.

Antonio López at the Speaker Corner 

The CVC researcher Antonio López gave a talk at the Speaker Corner on the development of autonomous vehicles and the current challenges in training artificial intelligence systems for driving. Developing autonomous vehicles requires training and testing AI-based drivers using supervised data from a wide variety of driving scenarios. The talk highlighted the work carried out at the CVC and the UAB to reduce the need for manual data labelling, focusing on sensorimotor models trained through imitation learning.