PresentationThis program proposes graduate students 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 communication networks as well as to discover new emerging research topics. Two main objectives can be tackled when following this program. First, to master formal techniques (e.g., modelling, AI) for network analysis, the students will study novel techniques and tools to model and analyze complex future networks. Secondly, the students could also be interested by all the software engineering techniques (e.g. software defined networks) to compute, improve and master the keys of the development of distributed networks.
BackgroundComputer Science and good knowledge in Java.
First yearLevel: Master 1 Language of instruction: English Location: Telecom SudParis, Evry Cost: 4000 or 6000 euros (see more on Tuition Fees) Coordinator: Dr Natalia KUSHIK, Telecom SudParis The program enables students to acquire the necessary background in computer science and networks to pursue a second year of specialization. Laboratory time and projects offer students the opportunity to practice and understand concepts more easily. This first-year program enables students to:
- Master formal techniques for communicating network analysis including new tools to model and analyze complex (future) networks
- Become familiar with software engineering techniques to compute, improve and grasp the subtleties of developing distributed networks
- Acquire the necessary background knowledge in computer science and networks to pursue the second-year CSN program.
The second-year Computer Science for Networks (CSN) Master’s program enables students to understand, analyze and improve communication networks, as well as develop and define software for next-generation networks. It provides techniques and tools to tackle current questions through the in-depth study of computer science and complex networks. Students will also learn to master recent approaches based on advanced software engineering.
The program comprises core courses in computer science and networks, as well as optional modules in specific domains. These courses are taught from September to mid-February. Lab hours and projects are scheduled for students to practice and assimilate concepts more easily. High-quality lectures and project supervision are provided by professors from renowned research labs and industry experts. The program has strong ties with industry, and many courses and lab sessions are run by our industry partners. The program also provides a first research experience to prepare students for a PhD or career as a research engineer in academic or industrial organizations.
Second year program
Master of Science tuition is set as follows for the 2022-2023 year:
|Year Course||Program||Students / situation||Tuition amount||Observations|
|MSc||All, except DANI||French or foreign EEA/EU nationals*||8000€||4000€/year|
|MSc||All, except DANI||Students coming from a partner university, other than above||8000€||Except Double degree 4000€/year|
|MSc||All, except DANI||Other than above||12000€||6000€/year|
|MSc2||MSc Data Science and Network Intelligence (DANI)||All||5000€||Joint degree|
Online application form. No application fee.
Official transcripts, degree certificate and/or completion certificate for Bachelor degree and Master’s degree (if applicable). Minimum GPA varies depending on the program.
- State of purpose
- Copy of passport
- Proof of English proficiency
- Recommendation letter(s)
Direct admission to the M2 program: Students holding a M1 or a Bachelor with at least 4 years (240 ECTS) may apply directly to the M2 program.
English proficiency requirement
For programs requiring English language proficiency, the minimum level is required for admission: CEFRL : B2 level
One of the following test scores is required for admission:
|TOEIC||750 / 790|
|TOEFL IBT||72 / 120|
|PTE Pearson||56 / 90|