Eindhoven University of Technology (TUE) is a university specializing in research and education in engineering science and technology, focusing on: (i) science for society – solving major societal issues and boosting prosperity and welfare; (ii) science for industry – the development of technological innovation in cooperation with industry; (iii) science for science – progress in engineering sciences through excellence in research and innovation. As part of the Brainport area, the technological heart of the Netherlands, the TUE frequently enters into special collaborative partnerships with companies and government authorities.

TUE’s Eindhoven AI Systems Institute (EAISI) mission aims to develop AI-technology for real-time autonomous decision-making in engineering systems that interact with humans by designing intelligent engineering systems that on-line sense their multimodal environment, learn and understand it, and reason about which action to take to achieve specified goals. TUE will invest in EAISI 100M Euro in the coming years and is aiming to attract funding of 30M/y. Over 100 academics are working already in EAISI and 50 new AI researchers will be hired. Two new AI master programs will be initiated. A joined research program will be setup in close collaboration with Brainport industries and beyond; EAISI is part of the AI NL Alliance. EAISI capitalizes on the scientific foundation of the AI applications which are cyber-physical in nature and positioned in the domain of engineering systems, which is primarily driven by industrial players. This requires applying AI algorithms to sensor data from complex physical systems. This is an area where advanced high-tech engineering systems meet AI.

TUE participates in the InShape project through the Interconnected Resource-aware Intelligent Systems (IRIS) group from the department of Mathematics and Computer Science. As an active group in EAISI, IRIS research aims to design, analyze, develop, and evaluate concepts, models, algorithms, protocols, and tools that optimize (distributed embedded) systems performance in terms of timing behavior, dependability, programmability, reliability, robustness, scalability, accuracy, energy and data computation efficiency, and trustworthiness. In this project, IRIS focuses on developing data driven methods for high-dimensional sequential and image data for optical systems (laser beam shaping) and the monitoring, control and optimization of the manufacturing process, in order to improve product quality and sustainability of the process.