Master the full AI cycle with an honest training program, designed to help you decide when to use the technology — and when not to.
It covers data spaces and sovereignty, advanced computing (edge, neuromorphic and real-time), machine learning, computer vision, multimodal perception and extended reality.
Finally, it addresses AI governance through responsible, transparent and secure models, combining theory and practice for the efficient development of solutions.
Professionals, teaching and research staff, and final-year students who want to advance their careers through cutting-edge technology: data spaces, advanced computing, algorithms and AI, and mixed realities, all under a rigorous responsible-AI governance framework.
No prerequisites, beyond fitting the target profile of this training.
Unlimited places.
Each learning unit follows the same method: real challenge → conceptual framework → demonstration/transfer to use cases → an honest look at when NOT to apply the technology. Delivered by specialists from ITG, ITI, Fundación CIRCE, CARTIF, IBV, Fidesol and Fundación CTIC.
Speakers · Module 1
Manages development projects and provides technical leadership for multidisciplinary teams. Experience in unmanned systems (UAS/drones) and data spaces, including the Industrial Data Space for Galicia.
Electronics engineer, responsible for European engagement in ITI's Innovation Ecosystems area. Works on data economy and data spaces, and is part of the Data Spaces Support Centre and the Big Data Value Association.
PhD in Telematics Engineering (UC3M / IMDEA Networks). Has coordinated ITI's Distributed Systems group since 2019; more than 40 research projects and 20+ international publications.
Speakers · Module 2
PhD in microelectronic design, with research stays at the Institute of Neuroinformatics in Zürich and at CERN. Has led ITI's Audio and Neuromorphic Processing group since 2021.
PhD in Physics, 20 years of experience in software for the energy sector. Develops software for digital substations under the IEC 61850 standard.
Computer engineer specialized in applied AI and Edge AI. Took part in the ASCENDER project with the Barcelona Supercomputing Center; designs multi-agent architectures and RAG systems.
Speakers · Module 3
PhD in Artificial Intelligence (University of Lorraine). Researcher at INRIA, Keele University and CARTIF; experience in Machine Learning, collective robotics and optimization.
More than 10 years of experience in data analysis, prediction and optimization algorithms. Leads European projects on AI applied to energy efficiency and digital twins.
Master's in Robotics and Artificial Intelligence. Experience in industrial robotics and automation; researches AI and reinforcement learning for energy efficiency and autonomous navigation.
PhD Industrial Engineer, researcher at IBV since 2004. Specialist in human movement analysis, ergonomic factors and data fusion methods.
Researcher at IBV since 2021. Working on a PhD on 4D scanners for human movement analysis.
Researcher at CARTIF with experience in collaborative robotics, human-robot interaction, extended realities and computer vision.
More than 20 years in software development and virtual reality. Project lead at Fidesol, driving AI initiatives in 3D environments and realistic avatars.
Speakers · Module 4
Director of the R&D Technical Support Area at ITI. Coordinates ITI's Responsible AI group, driving the adoption of ethical principles in AI projects.
Head of the Human Factor Unit at CTIC. Experience in virtual reality, AI and applied research centered on people.
PhD in Computer Engineering. Leads the enterprise AI line at CTIC, specialized in data governance, quality and security.
Free.
Yes. At the end of each module, a 10-question test (minimum 70% correct). The platform automatically issues the certificate in digital format.
The course runs on IBV's Moodle campus. Questions: campus.ibv@ibv.org
21 learning units, 15 specialists and an honest take on when to use AI — and when not to.
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