MATHS DISC
study programme

The proposed MATHS-DISC educational programme has the classical structure of 2 years articulated in 4 semesters/study periods.

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SPECIALISATION
DIGITAL AND GREEN TRANSITION
5 UNIVERSITIES
5 COUNTRIES
-
2 YEARS
4 SEMESTERS
120 ECTS
INTERNSHIPS
IN R&D LABORATORIES AND PARTNERS
SCHOLARSHIPS
EMJM, NDICI & OTHER FUNDING
WIDE NETWORK
EUROPE AND
WORLDWIDE

5 universities for 8 educational pathways

UNIVR
BUW
ISKPI
ULIS
UGA

CICO

Computational Intelligence & Complex Systems Optimization

Specialisation Path

UNIVR, ULIS or UGA

HPCQF

High Performance Computing Quantum And Computational Fluid Dynamics

Specialisation Path

BUW, UGA or UNIVR

SML

Scientific Machine Learning

Specialisation Path

ISKPI, BUW or UGA

FIN

Computational Finance & Circular Economy

Specialisation Path

BUW, ULIS or UNIVR

MED

Health and Biomedicine

Specialisation Path

ISKPI, ULIS or UNIVR

LOGT

Logistics & Transports

Specialisation Path

ULIS, UGA or UNIVR

AGR

Agrifood, fisheries, environment

Specialisation Path

ULIS, UNIVR or UGA

NRG

Energy Markets

Specialisation Path

BUW, UGA or ULIS

Specialize through the 8 main fundamental domains for the digital and ecological transition

The MATHS-DISC Committee helps each student in the choice of a personalized educational path, according to her/his mathematical background and her/his cultural and mobility preferences.

Each university offers you the possibility of specializing in specific areas. At the end of the first semester you must define  your specialisation path and the university where you will study during the third and fourth semester.

Study programme in detail

Semester 1

FOUNDATIONS/BACKGROUND

First semester may take place at any University of the MATHS-DISC Consortium

The first semester is dedicated to the fulfilment of some basic foundational mathematical requirements (FO) (differential equations, functional analysis, probability and statistics, numerical analysis) and to the acquisition of basic / background concepts in mathematical modelling (MO), computer programming and simulation (CS).

Choice of the Semester 1 University
Made by the Study Plan Committee (SPC) taking into account each student's mobility path wishes and previous mathematical background, in order to ensure a complete basic preparation and an even student distribution among the host Universities.

First semester activities will include virtual meetings and a joint MSODE seminar delivered weekly by invited scholars, accessible through the MATHS-DISC e-learning platform.

First semester courses are mainly foundational and compulsory, see the first semester modules table below.

First semester modules table

Course Type Area ECTS
Math. Modelling in the applied sciences Mandatory MO 6
Functional Analysis Mandatory FO 12
Partial Differential Equations Mandatory FO 6
Elective course in CS Elective CS 6
TOTAL 30
Course Type Area ECTS
Computer simulation 1 Mandatory CS 11
Computer science 1 Mandatory FO 9
Numerical methods 1 Mandatory FO 8
TOTAL 28
Course Type Area ECTS
English language Mandatory FO 1,5
Basics of Research Mandatory FO 2
Complex Systems Modelling Mandatory MO 6
Numerical methods of PDEs Mandatory FO 7
Reliability and risk theory Mandatory FO 3
TOTAL 31,5
Course Type Area ECTS
Numerical Functional Analysis and Optimization Elective FO 30
Numerical Methods for Ordinary Differential Equations Elective FO 30
Linear model analysis Elective MO 30
Multivariate analysis Elective FO 30
Computational Statistics Elective CS 30
Probability Theory Elective FO 30
TOTAL 30
Course Type Area ECTS
Object oriented & Software design Mandatory CS 3
Applied probability and statistics Mandatory FO 6
Signal and image processing Mandatory MO 6
Partial differential equations and numerical methods Mandatory FO 6
Geometric Modeling Mandatory MO 6
Language courses Mandatory FO 3
TOTAL 30

Semester 2

METHODS

Acquisition of a solid knowledge

The second semester is dedicated to the acquisition of a solid knowledge along the main axes of the programme: Computing & Simulation (CS), Modelling & Optimisation (MO),  Data Science & AI (DA).

More precisely, two slightly different profiles are proposed, which share a basic training in DA:

  • The first emphasises Computing & Simulation (CS) methods and takes place at BUW.
  • The second emphasises Modelling & Optimisation (MO) methods and takes place at UNIVR.

The courses at BUW are mainly compulsory, while at UNIVR students can choose between deterministic or stochastic Modelling & Optimisation (MO) methods.

Second semester modules table

Course Type Area ECTS
Optimization Mandatory MO 6
Numerical methods for PDEs Elective CS 6
Stochastic calculus Elective MO 6
Numerical Modelling and Optimization Elective MO 6
Statistical Learning Elective DA 6
Numerical methods for PDEs Elective CS 6
Stochastic calculus Elective MO 6
Numerical Modelling and Optimization Elective MO 6
Statistical Learning Elective DA 6
Analytical Mechanics Elective MO 6
Statistical Learning Elective DA 6
One elective course Elective MO-CS-DA 6
One elective course Elective MO-CS-DA 6
Sustainable Entrepreneurship Elective 6
TOTAL 30
Course Type Area ECTS
Computer simulation 2 Mandatory CS-DA 13
Computer science 2 Mandatory DA 3
Numerical methods 2 Mandatory CS 8
Atmospheric physics 1 Elective MO 8
Computational electromagnetics 1 Elective MO 8
Computational finance 1 Elective CS 8
Computational fluid mechanics 1 Elective CS 8
Experimental Particle Physics 1 Elective MO 8
Imaging in medicine 1 Elective DA 8
Materials science Elective MO 8
Theoretical chemistry Elective MO 8
Theoretical Particle physics Elective MO 8
TOTAL 32

Semester 2

MASTER WORKSHOP

Designed to foster interdisciplinary teamwork

At the end of the second semester, during the summer period (usually by end July), the whole students cohort meets for an intensive two-week Master Workshop organised at one of the Universities or at an ECMI teaching centre. Student participation is mandatory and is fully refunded. The MW activities consist of: 

  1. Team activities on industrial problem modelling (cf. ECMI Modelling Week).
  2. Presentation of second year internship/Master thesis opportunities at the academic and  industrial Consortium partners.
  3. Crash courses or lectures on urgent topics.
  4. Seminars on societal and industrial challenges.
  5. Tutoring, mentoring, networking activities within the Master Alumni Association.

The Master Workshop is designed to foster interdisciplinary teamwork, cooperation and communication skills, and also to pave the way for each student’s second year's major cultural choices. It is also a strong unifying moment for students to exchange opinions and experiences and to build the future MATHS-DISC Alumni Association.

Semester 3

SPECIALISATION

Advanced courses in the 8 different specialised educational pathways

The third semester is dedicated to advanced courses in the 8 different specialised educational pathways (SP) related to the CS-MO-DA enabling technologies, which also provide a specific background preparation for the final Master's thesis (MT) project.

Each SP and most  MT topics are offered by several Universities, so as to respect student’s wishes on study and mobility plans.

Third semester courses are mainly elective and have to be chosen in accordance with the SP followed by the student.

An overview of the third semester courses offered by each University for the different SPs can be found in the tables of third semester modules below.

Other elective courses can be found in the general course catalogue of each University and are therefore not listed in these module tables.

Third semester modules table

Course Type Area, EP ECTS
Discrete optimization and decision making Elective CICO, LOGT, AGR 6
Foundations of Data Analysis Elective DA 6
Mathematical modelling in the applied sciences Elective NRG, AGR, MED 6
Computational Game Theory Elective CICO, LOGT 12
Logistics, operations and supply chain Elective LOGT 12
Mathematical finance Elective FIN, NRG 12
Numerical methods for mathematical finance Elective FIN, NRG 12
Statistical Models for Data Science Elective DA 12
Data Fitting and reconstruction Elective CS 12
Statistical methods for business intelligence Elective FIN, NRG 12
Machine learning for data science Elective DA 12
Reinforcement learning Elective CICO 12
Data Visualization Elective DA 12
Parallel programming Elective CICO 12
Mining Massive Datasets Elective DA 12
Natural Language Processing Elective CICO 12
Advanced Programming for AI Elective CICO 12
One elective course Elective ANY PATH 6
Complementary activity/transverse skills Elective ANY PATH 4
Master thesis Master thesis 32
TOTAL 60
Course Type Area, EP ECTS
Computer simulation 3 Mandatory HPCQF, FIN, NRG 4
Computer science 3 Mandatory CS 12
Numerical methods 3 Mandatory HPCQF, NRG 6
Atmosferic physics 2 Elective CS 8
Computational electromagnetics 2 Elective HPCQF 8
Computational Finance 2 Elective FIN, NRG 8
Computational Fluid Mechanics 2-5 Elective HPCQF 8
Experimental Particle Physics COS-DET-GESTA Elective CS 8
Imaging in medicine IMG2 Elective MED 8
Material Sciences NMvM Elective NRG 8
Theoretical Chemistry TC2 Elective MO 8
Theoretical Particle Physics COS-EQFT-FGM Elective MO 8
Master thesis 30
TOTAL 60
Course Type Area, EP ECTS
English language Mandatory 1,5
Pedagogy Mandatory 2
Big data mining Mandatory SML 4
Project management Mandatory FIN 4
Applied modelling Mandatory MED 5
Econometrics Elective FIN 4
Bayesian statistics Elective FIN 4
Causal inference Elective FIN 4
Text mining Elective SML 4
Statistical methods of text analysis Elective SML 4
Natural language processing methods Elective SML 4
Research seminar Mandatory SML, MED 3
Thesis preparation Mandatory SML, MED 4
Internship + Master Thesis 26
TOTAL 61,5
Course Type Area, EP ECTS
Numerical Analysis of Partial Differential Eqs Elective CICO, MED 30
Numerical Optimal Control Elective LOGT 30
Computational Methods in Finance Elective FIN 30
Biostatistics Elective MED 30
Applied Bayesian Statistics Elective MED, AGR, LOGT 30
Reliability and Quality Control Elective AGR, LOGT 30
Introduction to Stochastic Processes Elective FIN, NRG 30
Time Series Analysis Elective AGR, LOGT, FIN, NRG 30
Mathematical Modelling and Applications Elective Any path 30
Mathematical Models in Biomedicine Elective MED 30
Mathematical Statistics Elective CICO, MED 30
Introduction to Mathematical Finance Elective FIN, NRG 30
Statistical Methods in Data Mining Elective Any path 30
Master Thesis 30
TOTAL 60
Course Type Area, EP ECTS
An introduction to shape and topology optimization Elective SML 30
Computational biology Elective MED 30
Efficient methods in optimization Elective CICO 30
Differential Calculus, Wavelets and Applications Elective CICO 30
Fluid mechanics and granular matter Elective HPCQF 30
Geophysical imaging Elective HPCQF 30
GPU Computing Elective HPCQF 30
Handling uncertainties in (large-scale) numerical models Elective CICO 30
Modelling Seminar Elective ANY PATH 30
Optimal transport: theory, applications and numerical methods. Elective CICO 30
Quantum Information & Dynamics Elective HPCQF 30
Software Development Tools and Methods. Elective CS 30
Statistical learning: from parametric to nonparametric models Elective DA 30
Temporal, spatial and extreme event analysis Elective MO 30
Advanced Machine Learning: Applications to Vision, Audio and Text Elective SML 30
Data Science Seminars and Challenge Elective ANY PATH 30
From Basic Machine Learning models to Advanced Kernel Learning Elective SML 30
Learning, Probabilities and Causality Elective SML 30
Mathematical Foundations of Machine Learning Elective SML 30
Natural Language Processing & Information Retrieval Elective SML 30
Research project/ Master Thesis Mandatory 30
TOTAL 60

Semester 4

INTERNSHIP/MASTER THESIS

Finally, the fourth semester is dedicated to the Master's thesis project, which addresses a problem related to the industrial and societal challenges for the digital and green transition towards a sustainable, resilient and human-centred industry and society.

The Master's thesis can be carried out in an academic research laboratory or institute, or during an internship with an associated partner, or even during another short mobility period.

Any semester

COMPLEMENTARY ACTIVITIES
TRANSVERSAL/SOFT SKILLS

Complementary elective courses not related to the subject of study or transversal skills courses, such as foreign language courses or courses on specific topics related to industrial innovation and societal challenges, such as start-up entrepreneurship, sustainable development, patent law, principles of circular economy, are organised each semester by the consortium Universities, taking advantage of their specific expertise in the field. They are delivered in hybrid mode and made available in the common centralised e-learning platform.

Complementary seminars and study groups involving european networks like ECMI and EU-MATHS-IN, industrial and academic partners are offered on a regular basis to identify problems, needs, requirements and opportunities arising from industry and local areas, as well as to exchange and share experiences, skills and best practices between the Universities.