The Master in Mathematical Engineering is an interdisciplinary engineering master centred on the notion of mathematical model that has become instrumental in engineering sciences. Through a training in modelling, simulation and optimization (MSO), the students learn to design, analyse and implement mathematical models to be applied to complex systems of the industrial or corporate world, and to create efficient strategies to optimize their performance.
The mandatory courses provide the students with the necessary common skills in MSO. They span the domains of numerical analysis and scientific computing, dynamical systems, matrix computations, stochastic models, optimization models and methods.
Students are moreover offered several coherent lists of courses, called “options”. Some of the options provide them with advanced skills in various branches of MSO: optimization and operations research, dynamical systems and control, and computational engineering. The other options pertain to data science, financial mathematics, cryptography & information security, biomedical engineering, business risks and opportunities, and launching of small and medium-sized companies.
Below is the competency framework common to all the engineering masters. The Master in Mathematical Engineering distinguishes itself by the interdisciplinary engineering scope of the competencies and by the fact that modelling-related competencies are strengthened by the strong MSO background acquired by the students.
On successful completion of this programme, each student is able to :
1.2 Identify and use appropriate modelling and calculation tools to solve problems
1.3 Verify the plausibility and confirm the validity of results
2.2 Model the problem and design one or more original technical solutions that correspond to the specifications note
2.3 Evaluate and classify the solutions in terms of all the criteria found in the specifications note: efficiency, feasibility, quality, ergonomics and environmental security
2.4 Implement and test a solution through a mock up, a prototype or a numerical model
2.5 Formulate recommendations to improve the operational character of the solution being studied
3.2 Propose a model and/or an experimental device in order to simulate or test hypotheses relating to the phenomenon being studied
3.3 Write a cumulative report that explains the potential of the theoretical or technical innovations resulting from the research project
4.2 Collaborate on a work schedule, deadlines and roles
4.3 Work in a multidisciplinary environment with peers holding different points of view; manage any resulting disagreement or conflicts
4.4 Make team decisions and assume the consequences of these decisions (whether they are about technical solutions or the division of labour to complete a project)
5.2 Present your arguments and adapt to the language of your interlocutors: technicians, colleagues, clients, superiors
5.3 Communicate through graphics and diagrams: interpret a diagram, present project results, structure information
5.4 Read and analyse different technical documents (rules, plans, specification notes)
5.5 Draft documents that take into account contextual requirements and social conventions
5.6 Make a convincing oral presentation using modern communication techniques.
6.2 Find solutions that go beyond strictly technical issues by considering sustainable development and the socio-economic ethics of a project
6.3 Demonstrate critical awareness of a technical solution in order to verify its robustness and minimize the risks that may occur during implementation.
6.4 Evaluate oneself and independently develop necessary skills for “lifelong learning” in the field