Explore the programs and courses offered by Master’s Degree in Applied Mathematics
Browse Programs Admission InformationThe aim of the courses in this semester is to:
· Develop a strong command of the fundamental tools of functional analysis, understand the proofs of key results, and use them to solve various problems, particularly those arising from partial differential equations.
· Present the fundamental concepts of convex optimization.
· Provide statistical methods and the theoretical foundations necessary to solve common models in various fields such as insurance, agri-food, biology, and telecommunications systems. Practical sessions will help students become familiar with existing software tools and also create their own databases.
· Learn some methods of complex analysis.
The objective of the courses in this semester is to:
· Familiarize students with numerical methods for solving partial differential equations (PDEs).
· Introduce students to the mechanics of continuous media in general, and more specifically, to fluid mechanics, elasticity, and viscoplasticity.
· Enable students to understand and formulate mathematical models in continuum mechanics and solve some problems using symbolic or numerical computation software.
· Model certain physical phenomena using fundamental types of ordinary and partial differential equations, and present some analytical and numerical solution methods for these equations.
· Study the basic concepts of numerical analysis and optimization.
· Enable students to acquire numerical and algorithmic techniques and methods for solving real-world optimization problems.
· Raise awareness among students about the risks of corruption and encourage them to contribute to the fight against it.
The aim of the courses in this semester is to:
· Introduce students to analytical methods in mechanics.
· Provide students with foundational knowledge of classical calculus of variations and optimal control theory and their applications.
· Enable Master’s students to identify a problem (related to operational research), recognize classical problems, and use appropriate solution tools.
· Study evolution problems of first and second order: variational formulation, existence, uniqueness, and regularity of solutions.
· Study abstract parabolic problems (existence, uniqueness, and approximate solution techniques).
· Examine selected examples.
Semester 4 is dedicated to an introductory research project carried out in a research laboratory or a company, culminating in the writing of a thesis and an oral defense.
• Méthodes d’Analyse Fonctionnelle
• Optimisation
• Statistiques
• Méthodes d'analyse complexe
• EDP et Analyse Numérique des EDP
• Mécanique des milieux continus
• Modélisation
• Modélisation Stochastique
•Corruption et déontologie de travail
• Méthodes Fonctionnelles et Numériques en Mécanique
• Calcul des variations et théorie du contrôle
• Recherche Opérationnelle
• Introduction aux problèmes d’évolution
· Fundamentals for the study of function spaces, providing the theoretical basis for partial differential equations (PDEs) and optimization.
· Study of deterministic methods for convex, linear, and nonlinear optimization — essential for artificial intelligence, control systems, and cybersecurity.
· Introduction to inferential statistics, fundamental tools for data analysis, stochastic modeling, and machine learning.
· Study and numerical solution of PDEs — the core of physical modeling and computational engineering.
· General methodology for translating a real-world problem into a mathematical model — an interdisciplinary approach.
· Stochastic processes, Markov chains, probabilistic models — crucial in finance, cybersecurity, and epidemiology.
· Study of dynamic equations (ordinary and time-dependent partial differential equations) — foundational for physical and biological systems.
The current application of Articles 171 and 1023 of the decrees stipulates that: