The first semester is a 12 week (September to January) program core curriculum designed to ensure competence in applied mathematics and basic computer science. The program is composed of:
The second semester is composed of a 12 week (February through May) academic program, followed by participation in at least 8 weeks internship in a research group. The academic program combines advanced work on fundamental topics as well as introduction to more specialized subjects. It also includes a research project. The program is composed of:
Download the academic Planning 2022-2023
The courses will start Moday 29, August 2022.
During the first two weeks, there will be remedial and compulsory background mathematics and computing sciences classes, during which administrative interviews will be planned with the staff.
The First term will start immediately after.
The aim of this course is to give mathematical grounds and algorithms for the modelling, animation, and synthesis of images.
The aim of this course is to provide basic knowledge of applied probability and an introduction to mathematical statistics.
The aim of this course is to give an introduction to numerical and computing challenges of large dimension problems.
This course is an introduction to the differential geometry of curves and surfaces with a particular focus on spline curves and surfaces that are routinely used in geometrical design softwares.
A student selected in the Graduate School track has to do a research project.
The aim of this course is to give mathematical grounds of security, integrity, authentication and cryptology.
The main objective of this course is to provide basics tools in operations research
This program combines case studies coming from real life problems or models and lectures providing the mathematical and numerical backgrounds
Introduction to computer sciences basics in the context of applied mathematics
Give an overview of modelling using partial differential equations.
The aim of this course is to provide the basics mathematical tools and methods of image processing and applications.
The aim of this course is to present the statistical approaches for analysing multivariate data. The information age has resulted in masses of multivariate data in many different field
The aim of this course is to get deep knowledge of PDE modelling and their numerical resolution, in particular using variational methods such as the Finite Elements method.
Heads of the program: Sylvain Meignen and Boris Thibert
Administrative contact: Emmanuel Villemont and Bérengère Duc
M1 AM: m1am (at) ensimag (dot) fr