Requirements for awarding master degree in Mathematical Engineering: 

  • Completion of 120 ECTS of graduate course work
  • Successful completion of a thesis
  • Successful completion of the final examination

Program duration: 2 years

Language of study: English, Russian, Uzbek, Kazakh

The Goal of the Master Programme: 

The master program trains professionals with strong engineering skill and solid mathematical knowledge; especially suitable for work within research and design groups that need in-depth design studies, advanced mathematical procedures, complex mathematical models and require
heavy and complex simulations.

The degree is designed to enable students:

  • to identify and deduce the mathematical model to adopt; the model based on a trade-off between the desired accuracy and the affordable complexity;
  • to search for a satisfactory consistency with the reality and optimizing the computational costs, in terms of time and resource usage while solving the problem;
  • to use the most recent numerical methods, visualization strategies to report results to colleagues in other disciplines.

The master programme specific objectives: 

  • to train a professional who can integrate engineering technologies and mathematical knowledge to define and solve complex problems that require all the modern design tools: mathematical modelling, computer simulations and statistical investigation;
  • to empower a learner with the competence to address problems from various engineering fields relating to (both) artificial systems, constructed or designed by humans, and to natural systems and phenomena.

The program aims to provide graduates with the skills neededfor the following activities:

  • choosing the best mathematical model to use, based on a trade-off between desired accuracy and accepted complexity;
  • performing qualitative and quantitative analysis of output generated by the model and evaluating the conformity with the phenomenon to be analyzed;
  • performing numerical simulations of natural phenomena, industrial processes, behaviors of materials and structural design;
  • performing analyses of statistical data, synthesizing them, adapting them to stochastic models of interest in the applications, their implementation for forecasting purposes in reliability and decisional analyses;

The curriculum is intended to ensure all (the) cognitive tools necessary to practice the career of a mathematical engineer, such as: 

  • Mathematical modelling, aimed at deducing the most suitable mathematical model to describe applicative problems and to analyze their solutions from both a qualitative and a quantitative point of view;
  • Numerical simulation, aimed at describing the most suitable methods for approximating the solution as well as the methods for graphically representing numerical solutions;
  • Probability and statistics applied to solving non-deterministic problems, managing, and interpreting data stemming from experiments or from probabilistic models;
  • Broad engineering skills for application in various sectors.

With the preparation and discussion of the thesis, students have an opportunity to put into practice all knowledge acquired, mixing the theoretical and the applicative and/or experimental skills as well as providing an original input.


  • Knowledge of the mathematical fundamentals of engineering, effective statistical methods to analyze and investigate data, comprehensive knowledge of the engineering fundamentals, engineering for business and economy.
  • Knowledge and understanding of the mathematical bases as well of the fundamentals of the hard sciences underlying all engineers training.
  • Detailed understanding of the natural and physical sciences as well of the engineering fundamentals applicable to the practice area.
  • Deep understanding at the procedural level, of the mathematics, numerical analysis, statistics, computer and information sciences, which underpin the practice area.


  • Ability to apply knowledge to the resolution (identification, formulation, resolution) of engineering problems (products, processes and methods).
  • Ability to solve problems in satisfying the constraints; (economic, environmental, social, political, ethical, health and safety).
  • Mastery of the theoretical and experimental tools for solving problems, be aware of their limitations and their implications for non-technical, understand their ethical and professional consequences.

Transferable skills: 

  1. (to) interact effectively as an individual and as a member of a team;
  2. (to) use an ample variety of different methods to communicate effectively with the engineering community and with society at large;
  3. (to) demonstrate awareness of the health, safety and legal issues and responsibilities of engineering practice;
  4. (to) demonstrate awareness of project management and business practices;
  5. 5. (to) engage in independent life-long learning.