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The master鈥檚 track in Edge Intelligence (EI) equips students with the knowledge, skills, and hands-on experience to design, develop, and optimize intelligent embedded systems operating at the network edge. It focuses on integrating artificial intelligence into resource-constrained devices, enabling real-time data processing directly where it is collected. This shift from centralized cloud solutions to decentralized, local intelligence makes systems faster, more efficient, and more secure.

What makes this track unique is its interdisciplinary setup. Offered jointly by the Departments of Mathematics & Computer Science (M&CS) and Electrical Engineering (EE), the program combines the strengths of software and hardware expertise. Students will learn to work across disciplines, preparing them to tackle the challenges of edge computing and embedded AI in a wide range of applications.

Graduates of the program will be able to push the boundaries of edge intelligence in areas such as smart manufacturing, intelligent healthcare, agriculture, mobility, and smart environments. With its strong focus on sustainability and privacy, the track not only addresses urgent technological challenges but also contributes to solutions with real societal impact.

Highlights of the program

• Interdisciplinary approach: A joint track by M&CS and EE, combining software and hardware expertise.
• State-of-the-art research: Courses and projects embedded in cutting-edge research on embedded systems and edge intelligence.
• Strong industry connections: Collaborations with Brainport companies such as ASML, Philips, Signify, NXP, ThermoFisher, and Canon.
• Hands-on learning: Access to embedded hardware (e.g., Raspberry Pi, Arduino, FPGA) and extensive lab-based assignments.
• Societal impact: Focus on sustainable, privacy-preserving, and resilient intelligent systems that reduce reliance on cloud infrastructure.