Duration
4 semesters
Component
Faculty of Science & Technology
Location(s)
Orleans
Presentation
This master's program offers a " Minerve Excellence pathway ". This is a selective "Graduate Program ofExcellence", GPEx, which is part of the "MINERVE" project FRANCE 2030 "ExcellencES sous toutes ses formes" obtained by the University of Orléans. Based on the traditional disciplinary Master's course(s), and anchored in the research community of universities and research organizations, this program offers training reinvented BY and FOR Research, with a multidisciplinary and digital orientation , leading to a career as a research engineer or researcher via the doctorate.
Students selected for the Minerve GPEx Excellence pathway will benefit from :
- personalized modularity of teaching: 80% of courses from the chosen disciplinary Master's program, 20% of high-level complementary units offered "à la carte", to be chosen from other Master's programs, and 20% of additional trans- or inter-disciplinary units;
- access to innovative educational, technical and digital tools ;
-continuous immersion in a research laboratory/department during the Master's program;
- personalized support, developed as part of this project;
- a collaborative project mode ;
- an M1 excellence scholarship;
- funding to complete the M2 apprenticeship in a research laboratory.
In addition to the disciplinary Master's degree, this course will lead to the award of an additional DU "Diplôme Universitaire Minerve" (Minerva University Diploma ).
The Master's degree in Applied Mathematics and Statistics - "Statistics & Data Science, Mathematical Engineering" specialization aims to train Data Scientists engineers, statisticians or applied mathematicians who can work in :
- statistical analysis services (banking, insurance, business intelligence, statistical services)
- the health and environment sectors
- Research & Development (R&D) services in leading-edge industrial sectors (Engineering, Learning, AI)
- Applied research, in particular by pursuing doctoral studies as part of applied theses (CIFRE type) in statistics (bio-statistics, epidemiology) or mathematics (numerical methods for models in physics, biology, AI).
Skills
Skills acquired :
- Random modeling (Decision mathematical statistics, Stat istical Computing)
- Data mining (data mining methods, learning, high-performance computing)
- Big Data (distributed computing with Hadoop clusters and MapReduce programming)
- Learning, Neural Networks (computer vision, machine learning)
- Applied probability (Monte Carlo simulation, random processes, MCMC algorithms)
- Mathematical modeling, scientific computing, optimization
- Proficiency in specialized software (R and SAS for statistics, Python, Matlab and C++ for scientific computing).
Useful contacts
Faculty of Science & Technology
Mathematics Department
1, rue de Chartres 45067 Orléans cedex
Minerve U-GPEX Excellence Program :
aide.minerve @ univ-orleans.fr
https://www.univ-orleans.fr/fr/minerve/decouvrir
Department secretary :
secretariat-maths.st@univ-orleans.fr
Admission
Admission requirements
This Excellence Minerve Master's program recruits in M1 only for the 2-year Master's cycle. The Excellence Minerve pathway is a demanding one, requiring an excellent disciplinary level, a pronounced taste for research and multidisciplinarity, and a clear desire to pursue a career in research through a doctorate.
M1 access only:
- Admission to M1 for students holding a Bachelor's degree in the discipline or equivalent from any French university.
- Admission to M1 by application for other students, in particular holders of another bachelor's degree from the school or other universities, a foreign equivalent or an adapted BUT3.
Maintenance available if required.
How to register
M1 application form to be sent to the following address:
Tuition fees
For students:
https://www.univ-orleans.fr/fr/univ/formation/droits-dinscriptions
For adults returning to school; consult SEFCO.
And then
Further studies
Possible doctoral studies
Professional integration
This Master's degree will enable you to work in statistical analysis and R&D departments in leading-edge industrial sectors; in banking, insurance and financial circles; in applied research in statistics (bio-statistics, epidemiology, reliability) or mathematics (numerical methods for models in physics, biology, etc.) via theses in an industrial environment, for example.