MAMME-INP Grenoble (Grenoble, France)

MAMME-ENSIMAG master’s double degree

At most 4 MAMME students each academic year will be able to do a joint master double degree and obtaining

Master Degree on Advanced Mathematics and Mathematics of Engineering (MAMME) at FME- UPC
Master Degree the Industrial and Applied Mathematics (MSIAM) at the Grenoble INP Ensimag - UGA

Legal details can be found here (Document 1, Document 2, Document 3).

Structure

The double master’s degree has a duration of three semesters, 90 ECTS, with the following general structure:

- First semester done at FME UPC (courses).

- Second semester done either at FME-UPC or Grenoble INP-UGA (master thesis)

- Third semester done at Grenoble INP-UGA (courses)

Study plan and intensifications

We propose 4 different study plans

Computer vision and graphics
Mathematical Modelling
Machine learning
Mathematical optimization

Path 1: Computer vision and graphics

First semester (at FME-UPC)

* Graph Theory (7.5 ECTS)

* Discrete and Algorithmic Geometry (7.5 ECTS)

* Numerical Methods for PDEs (7.5 ECTS)

* Mathematical Models with PDEs or Mathematical Models in Biology (7.5 ECTS)

Second semester (at FME-UPC or Grenoble INP-UGA)

* Master Thesis (30 ECTS)

Third semester (at Grenoble INP-UGA)

* Signal and Image Processing (6 ECTS)

* Geometric Modelling (6 ECTS)

* 3D Graphics (6 ECTS)

* Advanced Imaging (3 ECTS)

* Level set methods and optimization algorithms with applications in imaging (3 ECTS)

* Model exploration for approximation of complex, high-dimensional problems (3 ECTS)

* Geophysical imaging (3 ECTS)

Path 2: Mathematical modelling

First semester (at FME-UPC)

* Mathematical Models in Biology (7.5 ECTS)

* Numerical Methods for Dynamical Systems (7.5 ECTS)

* Numerical Methods for PDEs (7.5 ECTS)

* Mathematical Models with PDEs (7.5 ECTS)

Second semester (at FME-UPC or Grenoble INP-UGA)

* Master Thesis (30 ECTS)

Third semester (at Grenoble INP-UGA)

* Signal and Image processing (6 ECTS)

* Computing Science for big data and HPC (6 ECTS)

* Variational methods applied to modelling (6 ECTS)

* Wavelets and applications (3 ECTS)

* Non-smooth convex optimization methods (3 ECTS)

* Numerical optimal transport and geometry (3 ECTS)

* Temporal and spatial point processes (3 ECTS)

Path 3: Machine learning

First semester (at FME-UPC)

* Graph Theory (7.5 ECTS)

* Discrete and Algorithmic Geometry (7.5 ECTS)

* Numerical Methods for PDEs (7.5 ECTS)

* Mathematical Models with PDEs or Mathematical Models in Biology (7.5 ECTS)

Second semester (at FME-UPC or Grenoble INP-UGA)

* Master Thesis (30 ECTS)

Third semester (at Grenoble INP-UGA)

* Object oriented & software design (6 ECTS)
* Applied Probability and Statistics (6 ECTS)
* Statistical Analysis and document mining (6 ECTS)
* Machine learning fundamentals (3 ECTS)
* Inverse problem and data assymilation: variational and Bayesian approaches (3 ECTS)
* Kernel methods for machine learning (3 ECTS)
* Reinforcement learning (3 ECTS)

Path 4: Mathematical Optimization

First semester (at FME-UPC)

* Graph Theory (7.5 ECTS)

* Discrete and Algorithmic Geometry (7.5 ECTS)

* Numerical Methods for PDEs (7.5 ECTS)

* Mathematical Models with PDEs or Mathematical Models in Biology (7.5 ECTS)

Second semester (at FME-UPC or Grenoble INP-UGA)

* Master Thesis (30 ECTS)

Third semester (at Grenoble INP-UGA)

* Object oriented & software design (6 ECTS)
* Applied Probability and Statistics (6 ECTS)
* Statistical Analysis and document mining (6 ECTS)
* Nonsmooth Convex Optimization Methods (3 ECTS)
* Model exploration for approximation of complex, high dimensional problems (3 ECTS)
* Efficient methods in optimization (3 ECTS)
* Numerical optimal transport and geometry (3 ECTS)