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Programme duration: 2 years
Campus: Milano Leonardo
A Bachelor Degree in Mathematical Engineering or in a related area, with a solid background in the core disciplines: applied mathematics, computer science, physics, engineering.
This study programme accepts applications to 1st and 2nd semester. However, some key courses, preparatory for subsequent subjects, are offered only in the first semester. Therefore it is highly suggested to apply only to the 1st semester (September intake).
Mission and goals
This study programme aims at preparing professionals who are able to deal with complex design and managing problems by using advanced mathematical tools, yet with an engineering attitude. It combines a solid background in basic science with a sound knowledge of modern methods and technologies, with a persistent synergy between applied mathematics and engineering. When applying, the prospective student has to choose one out of the following three tracks: Computational Science and Engineering; Applied Statistics; Quantitative Finance.
Three available tracks. Applicants should specify their choice within their motivation letters.
- Computational Science and Engineering
Main topics: mathemathical and functional analysis, partial differential equations and their numerical treatment, fluid mechanics and computational fluid dynamics, algorithms and scientific computing
- Applied Statistics
Main topics: mathematical and functional analysis, stochastis processes, applied statistics, data analysis and statistical signal processing, Bayesian and computational statistics
- Quantitative Finance
Main topics: mathematical and functional analysis, stochastic differential equations and financial market models, mathematical finance, financial engineering, computational finance, data analysis and statistical signal processing
The professional opportunities opened by this programme are rather various and widespread: R&D divisions in manufacturing or engineering companies that deal with complex computational problems or that anyway need advanced mathematical tools; banks, insurance companies and financial institutions making use of quantitative finance for risk analysis or forecast; companies that require statistical interpretation and processing of complex data, or scenario simulation; public and private research institutes and laboratories.