A programme at the interface between mathematics, machine learning, and engineering
The Maths in Action (MAEA) Master of Science in Engineering provides advanced training in applied mathematics, focused on modeling, numerical simulation, high-performance computing, and machine learning, to address complex industrial and scientific challenges.
The programme is based on a strong connection between:
- solid theoretical foundations,
- advanced numerical and probabilistic methods,
- concrete applications from research and industry,
- long R&D internship, offering true professional immersion.
Overall Structure of the Programme
The MAEA Master of Science in Engineering corresponds to a second year of Master’s (M2).
It is jointly offered by Mines Saint-Étienne and Jean Monnet University of Saint-Étienne.
The programme is structured around:
- high-level academic courses,
- projects and case studies,
- a research or R&D internship at the end of the programme.
Core Mathematical Foundations
Three courses form the core of the programme and provide a robust common foundation for all students:
🔹 Applied Analysis
- Functional Analysis
- Analytical Methods for Partial Differential Equations
- Mathematical Foundations of Continuous Models
🔹 Stochastic Modeling and Statistical Learning
- Advanced Probability
- Random Models
- Statistical Learning
- Uncertainty Management
🔹 Optimization and Machine Learning
- Convex and Non-Convex Optimization
- Numerical Optimization Methods
- In-depth Introduction to Machine Learning for Engineering
Cutting-Edge Specialization Modules
These courses address current issues encountered in laboratories and industrial R&D departments:
🔹 High-Performance Computing and Numerical Simulation
- Advanced Numerical Methods
- High-Performance Computing (HPC)
- Parallel Algorithms
- Exploitation of Multi-core and Distributed Architectures
🔹 Metamodels and Global Optimization
- Response Surfaces
- Metamodeling
- Design of Experiments
- Reduction of Complex Models
🔹 Advanced Statistical Modeling
- High-Dimensional Statistics
- Analysis of Data from Simulators
- Coupling of Deterministic / Probabilistic Models
An Approach Focused on Large-Scale Numerical Simulators
The programme addresses current challenges in engineering and research:
- physical simulators that are computationally expensive,
- a very large number of parameters,
- massive and uncertain data,
- need for optimization, sensitivity analysis, and uncertainty quantification.
Students learn to:
- efficiently operate complex simulators,
- reduce computation times,
- extract relevant information,
- combine mathematics, statistics, and machine learning.
Research or R&D Internship
The programme concludes with a long internship, typically carried out:
- in an industrial company (R&D department),
- in a public research laboratory,
- or within a public or private organization.
This internship enables students to:
- implement the mathematical methods studied,
- work on real-world problems,
- prepare for doctoral studies or direct entry into R&D.
Research-Based Training
The MAEA Master of Science in Engineering is designed as training through and for applied research:
- strong interaction with laboratories,
- demanding scientific supervision,
- development of autonomy and conceptualization skills,
- natural preparation for a doctorate.
For engineering students, this Master of Science in Engineering is a major distinguishing asset in the job market.
🔗 Syllabus
❗️ Applications are open from January to April.
Contacts
Academic Director of the Master of Science in Engineering
Administrative contact
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