Computer Science for Networks
CSN – What for?
Want to understand, analyze and improve your communication network? Want to develop and define software on top of next-generation networks? CSN provides techniques and tools that tackle these questions with deep studies of computer science and complex networks. Furthermore, and in order to deal with these objectives, graduate students complementary wish to master the recent approaches based on advanced software engineering.
The CSN program proposes core courses in computer science and networks and optional modules in specific domains. These courses are taught from September to mid-Februaryù. A number of labs and projects are scheduled for students to practice and assimilate concepts more easily. High quality lectures and project supervision are provided by expert professors and industrials. Most of the professors are scientifically recognized in their research community and members of CNRS labs. Furthermore, courses and labs provided by industrials illustrates the relationships between the taught methods and their applications in the industry. Apart from the acquired technical background, the objective of the Master CSN is to give a first research experience so students are next able to apply for a PhD thesis, research engineers in academic or industrial organizations.
Language of instruction: English
Oriented: oriented both Industry and Research depending on the chosen core courses
Duration: 1 year
Courses Location: Palaiseau
The M2 CSN program proposes to be initiated to research and to acquire strong practical and theoretical knowledge in the network and computer science area. The broad range of proposed modules gives students the opportunity to deepen their technical knowledge through computer science methodologies applied to networks as well as to discover new emerging research topics. Two main objectives can be tackled:
- to master formal techniques for network analysis,
- to study and apply advanced software engineering techniques (e.g., software defined networks, containerization, etc.) to compute, improve and master the keys of the development of distributed networks.
The M2 CSN program is divided in two semesters. The first one gets deeply into specialization with technical and research topics. This semester is mainly dedicated to the study of techniques, methods and tools provided by computer science to model, analyze and improve networks and distributed systems. Based on the choices of the courses made by the students, set of topics may define a CSN specialization such as Formal Methods for Networks Analysis, Algorithmic in Communication Networks, SW development for the Networks, or Security and Testing. The courses (UE) mentioned below define these topics.
The semester is built on 30 credits ECTS. Four UE are mandatory (with a total of 15 ECTS) and others have to be chosen by students among a broad choice allowing to target a specialization. Based on the students’ choices, objectives above mentioned can be reached.
In parallel to the followed courses, a research project aims at initiating students to research. This project runs all along the semester whose 12 hours are dedicated to the areas of management and effective communications.
The second semester is dedicated to the master thesis (30 ECTS) which is commonly realized in an industrial or academic organization (always remunerated). The master thesis might be research oriented in case it is organized in an industrial context.
- List of the CSN courses:
- Simulation and Metrology
- Middleware for distributed Applications
- Virtualization: Concept and implementation
- Introduction to Research
- Network Security and Privacy
- Network Analysis and Modeling
- Formal System Testing
- Wireless network and IoT
- High-Performance Computing
- Software Model based Testing
- Global Laboratory for Industry-Driven Software Development
- Centralized/Cloud-Based Radio Access Networks
- Algorithm analysis and Computational Complexity
- Software Defined Networks
- Machine Learning for Computer Networks and Services
Simulation and Metrology
The objective of this course is to familiarize the Master students with event-driven simulation concepts. Performance studies can be driven either analytically, by simulations or by measurements. Simulation is a flexible methodology that can be used to analyse and to model the behaviour of systems and networks. Nowadays, simulations and modelling are being widely used in different fields: industries, research Institutes and Universities. Simulation results are also often used to compare those analytically.
During this course, students will be introduced to simulation theory by practising with Network Simulator. Then, the main simulation concepts will be presented: different types of simulations, notion of scheduler, random number generation and confidence interval estimation. The course will be validated with a small project.
Lecturers: Pr Tulin Atmaca and Pr Michel Marot
Students are evaluated by a project.
Middleware for distributed Applications
The aim of this module is to develop skills for designing and implementing distributed applications using middleware technologies. At the end of this course, students should be able to choose the appropriate architectural style: appropriate broadcast algorithms, synchronous methods, Representational State Transfer (REST), component oriented middleware, distributed event-based system (DEBS) and to produce enterprise distributed applications.
Lecturers: Sophie Chabridon (TSP), Denis Conan (TSP), Michel Simatic (TSP), Chantal Taconet (TSP)
All the subjects of the module will be illustrated by research articles as well as practical labs. Through a micro-project, students will design and implement a distributed application.
- Middleware for distributed applications definitions, patterns and overview, (lectures, 3h)
- Component-based middleware with Java EE (lectures and labs, 9h)
- Synchronous methods with Web Services (lectures and labs, 6h)
- Representational State Transfer (REST) (lectures and labs, 6h)
- Distributed Event Based systems (DEBS) (lectures and labs, 6h)
- Distributed broadcast algorithms (lectures and labs, 9h)
- Micro project (labs, 3h)
- Presentation of a research article
- Results of labs and intermediary deliverables
- Final examination: microproject final deliverable and defense
Virtualization: Concept and implementation
The goal of this module is to present the different possibilities to virtualize system from both user-level and system-level point of views.
Éric Renault (TSP), Aravinthan Gopalasingham (NOKIA)
What is virtualization
History of virtualization
The different kinds of virtualization
Some elements on operating systems and compilation
- Virtualization tools
KVM, Virtualbox, etc.
Linux containers, Dockers, etc.
- Virtualization and implementation
Diversion of function calls
Diversion of system calls
Evaluated lab or mini-project
Introduction to Research
The main objective of the course is the presentation of the most important aspects of research. The student needs to be aware of the related difficulties, issues, as well as the possibilities to overcome them. During the class, the students should learn how to identify a research-oriented problem, how to investigate the current state of the art in the area, as well as how to present original approaches for its solution.
Lecturers: Dr. Natalia Kushik; Invited researchers from and external to Télécom SudParis
Defining the research. Applied and fundamental research. Creativity and Curiosity in Research. PhD or no PhD?
Fundamental research: problems of analysis and synthesis; modelling and decision making; recalling ‘classical’ models in networks and approaches to their analysis and synthesis.
Applied research: languages and platforms used in the current and future networks; related technologies and existing solutions.
Research paper preparation: tips and hints on putting the background, motivation, related work, and contributions; checking related best practices.
Preparing a paper as a joint work: repositories, content-oriented editors, etc.
Research presentations: 5-min briefing, conference talk, seminar, (invited) lecture; checking related best practices.
Poster preparation: tips and hints.
Thinking of a traineeship / PhD at Télécom SudParis? Research directions and possibilities.
This course is supported by the execution of Applied / Fundamental projects proposed by the researchers from Télécom SudParis, including (Assistant) Professors, PhD students, research engineers, etc.
The class also includes attending research seminars of different kinds, including tutorials, graduate students’ presentations, invited talks, etc. Seminars where the students are going to present their progress on individual / group projects, are also foreseen, as well as the practical work on the paper preparation.
The evaluation is performed based on the quality of the individual / group project implementation, as well as defined control points, namely: i) project progress presentations at the seminar and homework, including preparation of a part of research paper, presentation, state of the art, etc. (25 %), ii) final presentation – project defense (25 %), and iii) supervisor’s grade on the research project (50 %).
Network Security and Privacy
This course addresses both security and privacy in networks, under practical and theoretical dimensions. After positioning security vs privacy, some fundamental mechanisms are presented for securing networks (IPsec, SSL, VPN, PKI, filtering), and for preserving privacy in networks and digital identity management (TOR, blind and group signature). Useful basis as well as research aspects (RFID, blockchain) are given through labs, exercises and lectures.
At the end of the lectures, students are supposed to know:
• The fundamentals of security and privacy in networking area, and digital identity management
• security and privacy challenges in few hot research topics (RFID, blockchain)
• how to practically configure an IPsec VPN, and a traffic filter
• Useful basis as well as research aspects (RFID, blockchain) are given through labs, exercises and lectures. Optional labs are also proposed for students to practice on their own in the security and privacy domains.
Lecturers: Maryline Laurent (TSP), Olivier Paul (TSP), Joaquin Garcia-Alfaro (TSP)
• Introduction to security and privacy (course)
• Privacy and security models, digital identity, and anonymity of the IP traffic (course)
• Introduction to cryptography (course, exercises)
• Security protocols and VPN (course, lab, exercises)
• Traffic filtering (course, lab)
• Security, privacy and lightweight cryptography on RFID (course)
• Security protocol verification (course, exercises)
• Public Key Infrastructure (course, exercises)
• Security in cloud computing (course)
The evaluation includes a two-hours written exam and a lab.
The final grade is computed based on the following ratio: ¾ (exam) and ¼ (lab).
No extra exam is scheduled.
Network Analysis and Modeling
Over the past decade, there has been a growing interest for the complex « connectedness » of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication.
now takes place. Beyond this classical example, the Network science is a now thriving and increasingly important cross-disciplinary domain that focuses on the representation, analysis, and modeling of various connected systems such as social network, brain Networks, biological network, mobility and transport networks. Motivated by these developments in the world, there has been a coming-together of multiple scientific disciplines in an effort to understand how highly connected systems operate. Network science aims to capture, modeling and understanding networks and rich data requires understanding both the mathematics of networks and the computational tools for identifying and explaining the patterns they contain.
This graduate-level course will examine modern techniques for analyzing and modeling the structure and dynamics of complex networks. The focus will be on statistical algorithms and methods, and both lectures and assignments will emphasize model interpretability and understanding the processes that generate real data. Applications will be drawn from computational biology and computational social science. No biological or social science training is required.
Dr Vincent Gauthier
- Introduction and overview
- Network basicks
- Centrality measures
- Eigen centrally page rank
- Random graphs (simple)
- Configuration models
- Advanced random graph model
- Network resiliency
- Spreading processes
- Social Network analysis
- Community detection on networks
- Data wrangling + data sampling
- Student project presentations
The final grades will be weighted as follows:
Student project: 20%
Final Exam: 60%
Formal System Testing
The main objective of this course is to provide students with some knowledge in Modelling, Verification and Validation aspects. Testing techniques applied to functional or non-functional aspects (e.g. security) on new generation networks (e.g., ad hoc routing protocols) are emphasized.
A first step is to present what a protocol’s formal model is and how to obtain it. Therefore, languages and modelling industrial tools are introduced. The second step is to verify this model in order to finally, in a third phase, derive some tests to validate the real systems.
Lecturers: Pr Stephane Maag
Specification techniques for communicating protocols and services
A) model-based testing
B) ITU-T SDL and SySML
Techniques for automated generation of test scripts
A) from the models
B) field experience
Standardized test execution scores.
A) ETSI TDL and TTCN3
Network monitoring techniques applied to tests
A) passive test
B) DPI – Deep Packet Inspection
Industrial tools for testing
A) active / passive test tools
B) Monitoring tools for testing.
High Performance Computing
The goal of this course is to present the state of the art of high-performance computing in terms of system and software architectures and to highlight trends and open problems in this area.
After the presentation of an overview of HPC which includes the different kinds of architectures (clusters, grids, clouds), teachings focus on algorithmic, programming models, performance evaluation of parallel programs and programming languages (especially OpenMP and MPI). High-performance networks (like InfiniBand), checkpointing for parallel systems and SSI systems are also studied.
Lecturers: Pr Eric Renault
Written exam and continuous evaluation.
Software Model based Testing
The main objective of this course is the study of existing testing techniques for different types of software used in telecommunications. The students should learn how to estimate the software quality from a tester point of view and which formal models can be used to derive high quality tests or to verify the correctness of certain properties of software components of discrete event systems.
At the end of the course, the successful students should know and be able to effectually apply:
- Most popular test derivation strategies and their fault coverage;
- Existing active and passive testing techniques allowing to conclude about the software quality.
Classes are supported by research and development projects, implemented in groups or individually.
Lecturers:Drs. Natalia Kushik and Jorge López (TSP)
- Introduction to software quality (lecture)
- Introduction to software testing problem; testing assumptions and methodologies; classification of various testing techniques (lecture and exercises / laboratory);
- Motivation to Model Based Testing; introducing formal models for discrete event systems (lecture)
- State model-based test generation strategies (lecture and exercises / laboratory);
- Introduction to non-intrusive testing techniques (lecture and exercises)
- Static Analysis / Code Verification and Passive testing (lecture and exercises / laboratory)
- (Semi-) random test generation VS Model based (laboratory);
- Examples of existing tools implementing the test generation strategies of interest (laboratory);
- Individual / group project on software testing.
The evaluation includes a 3-hour written exam and a project.
The final grade is computed based on the following ratio: 3/4 (exam) and 1/4 (project).
Global Laboratory for Industry-Driven Software Development
The emphasis is on development of a prototype system in which software has a significant role. The software must be developed following a continuous integration approach based on agile development methods. The teams (made up of students located around the world) will be expected to deliver working software (to a real client) in a sequence of weekly sprints.
Prerequisites: Each team member must be able to program competently in a high-level programming language. They must also know the fundamentals of software engineering, including all aspects of the software life-cycle.
Lectures : Dr J Paul Gibson
This module proposal is part of the European project HUBLINKED. Global Labs are online modules where teams of international students work on software development/other prototypes, which are specified by industry or community partners, with the aim of ‘turning real-world ideas into experience-appropriate prototypes’. Student teams are mentored by both academic and industry staff.
This is not an industrial placement – the students continue to work in the academic environment.
The project will be developed over a time period of 12 weeks. Each team is expected to plan/schedule the work on a weekly basis. Every week the team must deliver a progress report, and an updated plan for the weeks ahead. The team must hold a meeting between team members at least twice a week; and a meeting with the academic advisor and/or industrial supervisor at least once a week.
The learning laboratories
Every week, the students will be expected to complete an on-line lab. explaining a useful technique/tool specific to the module in question. These can be completed individually and/or in teams.
The following learning objectives will form the basis of the evaluation –
1) Team work in a global context (using appropriate planning, communication and management tools)
2) Use of an industrial-strength version control system
3) Use of an industrial-strength continuous integration platform-service for agile development
4) Quality-assurance on delivered work
5) Interaction with an industrial client
The final mark will be calculated from:
Continual delivery of work (50%)
Engineering log journal (20%)
Participation in global learning labs (20%)
Centralized/Cloud-Based Radio Access Networks (C-RAN)
The aim of this module is to understand and analyze the main challenges for future centralized radio access networks. The courses will focus on enhancements techniques introduced in LTE-A networks where cooperation among base stations is strengthened in order to increase Quality of Service levels and radio spectral efficiency. The course will then go through the architectural evolutions from distributed to centralized cloud-based radio access networks (C-RAN) and analyze the key features and challenges of these evolutions. At the end of this course, students should be able to conceive, model and evaluate cooperation mechanisms for C-RAN and conduct optimization studies for future cellular networks.
Lecturers: Badii Jouaber (TSP) and other faculty member of TSP.
- Cellular network evolutions: from GSM to LTE-A (lectures, 3h)
- From centralized to distributed BS architecture: RRH and BBU splitting (lectures, 3h)
- C-RAN, H-CRAN architectures and issues (lectures, 3h)
- Interference remediation techniques (lectures, 3h)
o Inter-Cell Interference Coordination (ICIC)
o Enhanced Inter-Cell Interference Coordination (eICIC)
- CoMP: Coordinated Multi-Point techniques and scheduling (lectures, 6h)
- Joint transmission (JT), Dynamic point blanking (DPB), Dynamic point selection (DPS), Coordinated Beamforming
- Modelling and performance evaluations (lectures, 3h) + (home work 21h)
- Scheduling techniques, Clustering techniques
The evaluation includes a presentation of a personal research work and a final examination
Algorithm analysis and Computational Complexity
Computer Science does not live without algorithms, and furthermore, ‘good’ algorithms, and thus, their analysis is crucial. The successful students after this class should understand why big industrial players now put a lot of efforts into the algorithm analysis and should be able to perform such analysis by themselves.
In the context of the CSN program we will tackle the network related classical and non-classical problems in computer communications and will study how to perform their analysis.
The main objective of the course is therefore the study of the design and analysis of algorithms, including the proofs of their correctness and their complexity estimation.
Lecturers: Drs. Natalia Kushik and Jorge López (TSP)
Turing Machines. Computability. Decision problems. Decidable and undecidable problems. The Halting Problem (Lecture and exercises).
Algorithm and Problem Complexity. Complexity classes. Time and Space Complexity. Hardness and completeness of (decision) problems (Lecture and exercises).
SAT-problem (Lecture and exercises).
Algorithm analysis for “very known” problems (e.g., sorting) – where to start and how to proceed.
Hamiltonian path, Hamiltonian cycle problems, Clique problem (Lecture and exercises).
Data structures as a complexity reduction strategy.
Greedy algorithms as tempting ideas: how often do they lies and what is the price?
Algorithms and their analysis in related fields of Communication Networks:
– Complexity issues in information security (Lecture, discussion and students’ presentations) – background for the guaranteed secrecy.
– Complexity issues in software testing (Lecture, discussion and students’ presentations) – background for software certification.
Discussions and debates on open (Millennium) problems:
i) P =? NP.
ii) Alternative complexity definitions?
iii) Improving the performance of an implementation? ‘Classical’ compiler optimizations and beyond that; from high to low level programming languages?
Individual laboratories on the listed subjects will be performed. The algorithm analysis will be supported by the experimental evaluation of software quality parameters, such as performance, disc load, energy consumption, etc.
The evaluation includes a 3 hour written exam (1/2 of the final grade).
The class also contains the continuous evaluation represented as homework, laboratories and students’ presentations (1/2 of the final grade).
French as a Foreign Language
French courses aim to develop language skills, intercultural skills and learner autonomy. They are organized by level. Each course aims to reach one of the CEFR levels:
Level A0 = complete beginner
Level A1 = introductory or discovery level
Level A2 = intermediate or survival level
Level B1 = basic operational level
Level B2 = advanced level or independent user
Level C1 = advanced or experienced, autonomous user
For more information see the Common European Framework Reference for Languages (CEFR).
Lecturers: Jean-Claude Massey, Jalal Zaïm, Lucile Bertaux, Nathalie Ohayon
Courses are based on the communicative approach and action-oriented approach recommended in the CEFR. Students will carry out projects, tasks activities and exercises linked to the objectives of their language level. The intercultural approach is an integral part of courses. In addition learners are accompanied in developing learner autonomy.
Classes use ICT, authentic documents and multimedia. Besides class, significant engagement and personal work are required to make efficient progress.
CEFR : http://www.coe.int/t/dg4/linguistic/cadre1_en.asp
Assessment aims to determine what has been acquired, what is in the process of being acquired and what has not been acquired. It also takes into account class participation and personal work (continuous assessment). The final grade out of 20 is based on continuous assessment (60%) and the final exam (40%).
Machine Learning for Computer Networks and Services
- Understanding the main machine learning methods and algorithms
- Being able to apply them to computer networks and applications to solve practical use-cases
- Being able to define and follow a correct protocol (data pre-processing, training, test, validation) and to adapt it to the different use-cases
- Being able to use the main Python libraries for Machine Learning
Lecturers: Andrea Araldo (TSP)
1. Introduction (supervised / unsupervised Machine Learning, protocol, data preparation in python)
2. Data exploration, Linear Regression, evaluation of regression models
3. Neural Networks (application to network intrusion classification)
4. Anomaly detection (application to intrusion detection)
5. Recommender systems (application to recommendation of web content)
6. Time series, Preventive Maintenance, Long Short-Term Memory networks (application to IoT or data centers).
7. Project presentation and exam
All courses will be “cours intégrés”
50% project, 25% exam, 25% participation in class.
Software Defined Networks
This course presents, using theory and practice, the principles of software networks and virtualized and shared computer infrastructures and services. This evolution from static computer networks to programmable infrastructures and services using commodity hardware has been enabled by the emergence of the cloud, Software Defined Networks (SDN) and Network Function Virtualization paradigm. This course will focus on this evolution in terms of overall architecture, orchestration and management of programmable networks and of virtualized services and network functions as well as on the control of the data plane via SDN controllers and services managers. The management, control and data plane evolutions will be thoroughly analyzed, investigated and implemented by the students to get acquainted with the associated orchestration engines (e.g. TOSCA), SDN controllers (ONOS, ODL, Ryu, etc…) and the various de facto protocols, data models and interfaces across the planes (such as OpenFlow, NetConf/Yang, TAPI) and associated plug-ins and drivers. Related use cases in the context of corporate networks, 5G, inter-ISP are analyzed and investigated. Since the notion of placement of virtualized services and network functions are fundamental in programmable networks, optimal placement and resource provisioning algorithms in hosting and shared infrastructures will be addressed by the students during the course. They will analyze existing embedding, service function chaining and slicing algorithms in a multi-tenant context and propose their own via micro research projects. A more general picture of SDN and NFV in the context of Cloud Computing, Edge Computing, Mobile and Fog Cloud is also presented.
Lecturers : Djamal Zeghlache, Télécom SudParis
- Introduction to programmable networks (course)
- The SDN paradigm, separation of control and data plans and associated interaction protocols and configuration interfaces (course)
- Network configurability using NetConf and Yang (course)
- SDN configuration and practice using ONOS, OpenDayLight, Mininet and Wireshark (course, lab, exercises)
- SDN protection strategies (course, lab, exercises)
- SDN applications design and deployment issues (course, lab, exercises)
- Convergence between SDN, NFV and Cloud Computing (course)
- NFV paradigm, architecture and key system components with emphasis on management and orchestration of virtualized services (course)
- Slicing use case using an orchestrator, Kubernetes, ONOS and associated protocols (for interactions, configuration and control) via a lecture and practical work/practicals
- Research topics (course)
The evaluation includes a 3-hour written exam and one lab.
The final grade is computed based on the following ratio: 3/4 of exam grade) plus 1/4 of lab grade.
A second session exam is proposed based on the following ratio: best between (3/4 of exam grade plus 1/4 of lab grade) and (second exam grade).
Wireless network and IoT
- To learn and understand the challenges in the design of wireless networks.
- To learn about wireless networks’ architectures, protocols, technologies and QoS
- To learn about IoT ecosystem, uses cases, requirements and challenges, technologies and solutions
- To understand various security issues and challenges for IoT networks and learn about existing solutions.
Lecturers: Badii Jouaber (TSP)
Part I (Theory)
1. Introduction to Wireless communications and Internet of Things
2. Example of connected things and IoT Use cases
3. Physical networks: WPAN, WLAN, WMAN and WRAN
4. Short Range Networks: WiFi, Bluetooth, Zegbee, RFID, 6LowPAN
5. Device2Device Networks, Ad Hoc and multihop networks
6. Cellular Networks (From GSM to 5G)
7. Networking solutions for IoT (LTE-M, NB-IoT, Lora, SigFox, …)
8. Cloud and Fog Networking for IoT
9. Platforms for IoT
10. Security and Green aspects of the IoT
Part II (reading articles and presentation)
It consists of selecting articles related to the Wireless technologies and IoT, working on the articles and then providing presentation to audience.
Presentation research article
The Master CSN leads to research (in research institutes or universities) and engineering positions (e.g., industry) in the diverse fields of computer science dedicated to networks and communication systems. More specifically, the domains the students will be able to use their learned skills, knowledge and expertise on are the modeling and analysis of complex networks, distributed computing for new-generation communicating systems, their qualitative and quantitative studies.
Several research labs and companies hire students from CSN such as Nokia (Bell Labs), Orange (R&D), CEA, Thales, Huawei, etc. Students also have opportunities to continue with a PhD thesis in a CNRS Lab from Institut Polytechnique de Paris as well as in the industry (e.g., CIFRE).
- Télécom SudParis
- Télécom Paris
The M2 CSN has also signed agreements with other universities as mentioned in the following:
Beijing JIAOTONG University, China, Dual Master Degree agreement
Galileo University, Guatemala, Dual Master Degree agreement
The M2 CSN is also part of the European project HUBLINKED. In that context, the students following some of the modules proposed in the M2 CSN are awarded of a European label provided by HUBLINKED. Two other universities and corresponding partners are part of this European initiative:
Mälardalen University (MDH), Sweden
Technological University Dublin (TUD – ex. DIT), Ireland
Chairs and partnerships
- Chaire Cybersécurité des infrastructures critiques (Télécom SudParis et al.)
- Chaire Valeurs et politiques des informations personnelles (Télécom SudParis et al.)
- Chaire Inventivités Digitales (Télécom SudParis et al.)
- Chaire Réseaux du futur pour les Services de demain (Télécom SudParis et al.)
- Chaire Good In Tech (Télécom SudParis et al.)
Master Double Degree with JiaoTong University of Beijing, China (BJTU)
Master Double Degree with Galileo University, Guatemala (UGAL)
Label from European project and joint courses with universities through the ERASMUS+ HubLinked project.
- Completion of the 1st year of a master program (Master 1)
- Basic knowledge on network protocols.
- Basic knowledge in Object programming languages.
- Basic knowledge in Mathematics and Probability.
- Expected English level – B2 or higher (CEFR B2 ; IELTS: 5.5/9 ; TOEFL paper based: 550/677 ; TOEFL computer based: 213/300 ; TOEFL internet based: 79/120 ; TOEIC: 750/990 ; Cambridge: CAE (Certificate of Advanced English) ; CET-6 600)
- For non-English speakers, certificates proving the levels are mandatory
Deadlines for the Master application sessions are as follows:
– First session: February 28, 2020
– Second session: April 30, 2020
– Third Session (optional): June 30, 2020 (only if there are availabilities remaining after the 2 first sessions)
Applications not finalized for a session will automatically be carried over to the next session.
You shall receive an answer 2 months after the application deadline of the session.
International Master: EU students : 4250 euros / Non-EU students: 6250 euros