• Training structure

    Faculty of Science

Presentation

The Master's in Mathematics is divided into three first-year courses: Modeling and Numerical Analysis (MANU), Fundamental Mathematics (MF) and Statistics and Data Science (SSD). In the second year, the MF stream splits into two streams: Fundamental Mathematics and Preparation for the Agrégation; the SSD stream also splits into two streams: Biostatistics (SSD-BIOSTAT) and Information and Decision Management (SSD-MIND).

 

Please refer to the individual course descriptions for detailed presentations.

 

Read more

Objectives

- Acquire a scientific background enabling you to interact in a multidisciplinary context

 

- Acquire a solid theoretical background that may lead to an academic or industrial thesis.

 

- Preparing for the agrégation competitive examination

 

Read more

Know-how and skills

see course descriptions

Read more

Organization

Program

see course descriptions

Read more

Select a program

Modeling and Numerical Analysis (MANU)

See the complete page of this course

Fundamental Mathematics (FM)

See the complete page of this course

Preparing for the external agrégation in mathematics (Prépa Agreg)

This course is designed for students preparing for the agrégation (external or special) in mathematics. Its aim is to help students revise and prepare for the various competitive exams. The first semester is divided between revision of the concepts included in the competitive examination program and an introduction to the specific tests. The second semester begins by finalizing preparation for the written exams, followed by preparation for the oral exams. The ECTS credits acquired in the various training units are used to validate the M2 required for the competitive examination.

See the complete page of this course

Statistics and Data Science (SSD)

The SSD program is a course in applied mathematics that aims to provide high-level skills in statistics, random modeling and data science.
It is designed to provide solid knowledge and professional skills to enable students to join multidisciplinary teams in a wide range of sectors: health, biology, ecology, environment, genomics, energy, agronomy, economics, banking, insurance, marketing, research, higher education, etc.

See the complete page of this course

IDIL - Modeling Biological and Environmental Systems - Mention MATHEMATIQUES

The aim of the "Modelling Environmental and Biological Systems" (MoBiEn) Master's program is to train students in the quantitative and theoretical investigation of complex phenomena in living systems emerging at several scales: from the single molecule to living organisms, as well as their interaction with their environment.

The Master MoBiEn brings together scientists from different laboratories and professors from our 4 departments: Mathematics, Physics, Mechanical Engineering and Computer Science. In this sense, it offers a coherent multidisciplinary program that makes MoBiEn a unique training program whose core disciplines are Statistical Physics, Stochastic Processes, Biomechanics, Numerical Simulations, Statistical Methods, Advanced Data Analysis Techniques, and Artificial Intelligence.

Examples of teaching units :

- Stochastic processes
- Biological physics
- Finite element simulation

 

See the complete page of this course

Admission

Target audience

Students with a Bachelor's degree in Mathematics

Read more

Necessary prerequisites

Bachelor's degree in Mathematics or equivalent

Read more

Recommended prerequisites

Bachelor's degree in Mathematics or equivalent

Read more

And then

Further studies

Doctorate in specialties related to Mathematics and its applications

 

Read more

Professional integration

 

Mathematical engineering, including modeling, scientific computing, (bio)statistics and data analysis.

Mathematics teaching in secondary schools (collège, lycée, classes préparatoires aux grandes écoles) or higher education (university)

Research careers in fundamental mathematics, applied mathematics and statistics, in the public or private sector.

 

Read more