Mathematics and Applications

Overview

This specialisation provides a broad-based curriculum in applied mathematics, from the core notions in analysis and probability to more applied vocational concepts in statistics or scientific computing.

The multidisciplinary nature of this specialisation represents an advantage across a wide range of sectors requiring a sound understanding of mathematical tools and concepts in order to meet new technical and economic challenges. The aim is not only to provide a solid grounding in mathematics, but also a good grasp of topical issues. The lecturers lead research activities linked to different industrial sectors (aerospace, nuclear power, energy, environment, health, finance etc). This gives the students exposure to the practical applications of mathematical tools and concepts and possible career orientations.

Two tracks are on offer within the specialisation: 'Statistics and Data Science' and 'Numerical analysis and probability'.

Some courses are taught jointly in conjunction with the Master Mathématiques et Applications at the University of Nantes.

Admission

International students can follow this specialisation, taught in French, via:
 

  • A double degree programme - Open to international students selected by our partner institutions. Selected students spend two years studying courses from the engineering programme at Centrale Nantes. This usually includes one year of the common-core engineering curriculum followed by one year of specialisation. Double degree students are typically accepted after successfully completing two or three years of higher education in their home institution.
  • The fast-track engineering programme: Open to students with a Bachelor's or equivalent degree in science. Our fast-track programme gives international students who are qualified to bachelor level the opportunity to gain the 'diplôme d'ingénieur' in just two years.
Course Content
Autumn Semester Spring Semester
Core Courses:
Hilbertian analysis Advanced statistical learning
Statistical learning Uncertainty quantification
Probability Project 2
Probabilistic numerical methods Internship
Stochastic processes
Project 1
Numerical analysis and probability Track:
Numerical analysis Stochastic modelling
Partial differential equations Modelling for health and biology
Advanced numerical analysis
Statistics and Data Science Track:
Statistics Basics of statistical learning
Data Mining Bayesian methods and hierarchical models
Data Science with R
 

Download full syllabus 

 

NB 2019/20 Course content provided for information purposes only. 2020/21 syllabus currently in preparation.

Examples of projects and internships

Examples of Past Projects

 
  • Portfolio optimization
  • Monte Carlo methods for rare event estimation
  • Patterns of Alan Turing
  • Portfolio risk measures   
  • Population dynamics and breast cancer tumor growth modelling
  • Data mining for the analysis of petroglyphs
  • Numerical simulation of the transport of nuclear waste
  • Matrix completion for painting restoration
  • Multilevel Monte Carlo methods for option pricing
  • Study of the graph of Erdos Renyi
  • Numerical simulation of neural influx in neurons
  • Approximation power of deep neural networks
  • Introduction to quantum computing


Examples of past internships

 
  • Classification and Forecasting of load curves (GDF Suez strategy division)
  • Outsourcing of post-trade tasks (Accenture)
  • Integration of external variables to optimize hotel prices (Amadeus)
  • Development of a simulator (Thalès Alenia Space)
  • Reporting of investment funds (Prévoir)
  • Environmental characterization of the aircraft fleet (Safran)
  • Actuarial problems in reinsurance (Wills Re)
  • Reliability assessment of hybrid dynamical systems (EDF, Division Management of Industrial Risks)
  • Reporting of market risks for gas portfolio (EDF, Division Economy, Rate and Price)
  • Combination of statistical models for photovoltaic power forecasting (Reuniwatt)
  • Optimization of a statistical tool for sale forecasting (PSA)
  • Stochastic methods for the solution of high-dimensional PDEs (Centrale Nantes)
  • Passenger traffic forecasting models for decision supprt (SNCF)
  • Machine Learning applied to market abuse (HSBC)
  • NLP for automatic processing of legal documents (Stackadoc)
  • Optimization for precision viticulture (INRA)
  • Prediction of annuity revaluation costs (Generali)
  • Peptide retention time prediction (Functional Genomics Center Zurich)
After the specialisation

Industry sectors

 
  • Health
  • Environment
  • Finance
  • Insurance
  • Energy
  • Transport
  • Telecommunications


Career Prospects

 
  • Data scientist
  • Statistical engineer
  • Simulation engineer
  • Logistics engineer
  • Quantitative analyst
  • R&D engineer
  • Researcher
  • Banking/Insurance consultant
  • Actuarial analyst
Published on November 2, 2015 Updated on July 26, 2021