Programme & courses | CPS2 Master of Science in Engineering – AI & IoT

An international, multidisciplinary programme taught entirely in English

The Master Cyber-Physical and Social Systems (CPS2) is a programme of excellence that trains future experts in intelligent systems. The programme focuses on integrating the physical, digital, and social dimensions of complex systems (Internet of Things, Artificial Intelligence, and the Semantic Web).

Overall programme structure

Organised over two years (M1 and M2), the programme totals 120 ECTS credits and is delivered entirely in English, preparing students for an international career in research and innovation.
The programme includes:

core courses in advanced computer science;
specialised modules covering artificial intelligence, IoT, cloud/edge computing, cybersecurity, and socio-technical systems;
supervised projects and research projects;
a long research or engineering internship in M2 (4 to 6 months);
numerous interactions with partner laboratories and companies;
an international and multidisciplinary environment.

M1 provides a solid foundation in advanced computer science and distributed systems, while gradually introducing the key technologies of the Master of Science in Engineering.

Key M1 courses

Advanced programming (Python, Java, C/C++)
Algorithms and data structures
Networking fundamentals and protocols
Introduction to the Internet of Things
Databases & distributed systems
Introduction to machine learning
Web & mobile development
Complex systems modelling
Cybersecurity – fundamentals
Scientific English & communication

Projects

Team-based computer science project
Initial introduction to IoT / connected systems projects

Internship (optional depending on the case)

Option of a short internship or a laboratory immersion.

M2 is the core of the programme, with a step up in expertise in intelligent cyber-physical systems and socio-technical challenges.

M2 teaching areas

1. Internet of Things & Cyber-Physical Systems

IoT architectures
Communication protocols (MQTT, CoAP, LoRa, NB-IoT…)
Edge & Fog computing
Sensors, actuators, embedded systems
Digital twins (Digital Twins)

2. Artificial Intelligence & Data Processing

Machine learning applied to physical systems
Deep learning
Embedded AI & distributed AI
Signal processing & big data
Autonomous and adaptive systems

3. Web, Mobile & Ubiquitous Computing

Advanced web development
Mobile applications and ubiquitous systems
Human–system interaction (UX, privacy, trust)

4. Cloud Computing & Distributed Systems

Cloud architectures
Microservices
Containerisation (Docker, Kubernetes)
Distributed data management
Scalability & performance

5. Cybersecurity of CPS & IoT

Cybersecurity applied to physical environments
IoT security & IoT-by-design
Distributed systems security
Zero Trust architecture
Data protection and privacy

6. Social and behavioural dimensions

Privacy-by-design
Trust, adoption, and system acceptability
Socio-technical models
Impact of technologies on uses and communities

7. Scientific Skills and Research

Scientific writing
Research methodologies
Paper analysis and critical review
Preparation for doctoral studies

CPS2 project & team projects

Each year includes hands-on projects:

Development and securing of a complete IoT system
Modelling of a socio-technical system
Deployment of connected Web/mobile applications
AI + IoT integration in a real-world application

Final internship & Master’s thesis (30 ECTS)

The Master of Science in Engineering concludes with a 5- to 6-month internship, in a company or laboratory, leading to:

a scientific report,
an oral presentation,
a defence before an academic panel.

Internships are offered in:

digital companies,
research laboratories,
deeptech startups,
Industry 4.0 stakeholders,
IoT companies, smart city, smart building, mobility, energy, etc.

Teaching Approach and Research Environment

Mines Saint-Étienne provides students with a research-based learning environment focused on experimentation and collaboration:

Active learning
Theoretical teaching is complemented by intensive practical sessions and group projects that foster autonomy and multidisciplinary work.
State-of-the-art platforms
Students have access to the Institut Fayol platforms for experimenting with IoT architectures, developing Digital Twins, and applying Artificial Intelligence models.
Academic collaboration
The Master of Science in Engineering is the result of a collaboration with Université Jean Monnet and is affiliated with Université de Lyon, ensuring a high level of research and a broad academic network.