Intelligent systems at the network edge
Edge Intelligence responds to the growing need for computing systems that are fast, efficient, privacy-aware, and sustainable. Instead of sending all data to the cloud, edge systems process information closer to where it is generated; for example, in sensors, machines, vehicles, medical devices, or smart environments.
This makes intelligent systems more responsive, reduces energy use and data transfer, and can improve privacy. At the same time, bringing AI to resource-constrained devices creates new challenges: systems must be efficient, scalable, reliable, and able to perform under real-world constraints.
Jointly offered by the Departments of Mathematics & Computer Science and Electrical Engineering, the Edge Intelligence specialization equips you with the knowledge and hands-on experience to design and optimize intelligent embedded systems at the network edge. You bring your artificial intelligence and machine learning knowledge to build systems that can process data locally and respond in real time.
Degree structure
The specialization distinguishes itself from the main Embedded Systems program with a dedicated core program and specialization electives. It combines advanced coursework, hands-on experience, a graduation project, and opportunities for personal and professional development.
Core program
The core courses in the Edge Intelligence specialization shape you into an Edge Intelligence engineer with advanced expertise in distributed and embedded systems, IoT and edge computing, intelligent architectures, and approximate computing. You will apply the knowledge gained in these courses through hands-on assignments, particularly a problem-oriented FPGA project in the laboratory.
| Core Courses | Credits |
| Architecture of Distributed Systems | 5 ECTS |
| Networked Embedded Systems | 5 ECTS |
| Internet of Things | 5 ECTS |
| Embedded Computer Architecture | 5 ECTS |
| Edge Computing | 5 ECTS |
| Intelligent Architectures | 5 ECTS |
| Embedded Systems Laboratory | 5 ECTS |
| Approximate computing | 5 ECTS |
Electives
Elective courses give you the flexibility to tailor your profile and further develop your knowledge in fields such as machine learning, parallel algorithms, hardware design, and security.
| Elective Courses | Credits |
| System Validation | 5 ECTS |
| Advanced Algorithms | 5 ECTS |
| Automated Reasoning | 5 ECTS |
| Real-Time Systems | 5 ECTS |
| Massively Parallel Algorithms | 5 ECTS |
| Cyberattacks Crime and Defences | 5 ECTS |
| Wireless IoT Security | 5 ECTS |
| Quantitative Evaluation of Cyber-Physical Systems | 5 ECTS |
| Electronic Design Automation | 5 ECTS |
| Multiprocessors | 5 ECTS |
| Machine Learning for Systems and Control | 5 ECTS |
| System Design Engineering | 5 ECTS |
| Embedded Visual Control | 5 ECTS |
| Seminar IRIS | 5 ECTS |
| Seminar Formal System Analysis | 5 ECTS |
You also have free electives space where you can take a relevant course from 黑料福利网, TU Delft, University of Twente or abroad. You can also use your free electives to do an internship in industry or research (e.g. Brainport companies or international labs).
After graduation
Edge Intelligence graduates are prepared for careers at the intersection of AI, embedded systems, and hardware design.
Typical roles include IoT engineering, embedded AI development, cyber-physical systems, and high-tech R&D positions. Many also pursue PhD or EngD trajectories.
Graduates are highly sought after in the Brainport region, where companies such as ASML, Philips, NXP, Signify, Canon, and ThermoFisher develop next-generation intelligent systems.