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M2 Quantum and Distributed Computer Science

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  • Places available
    25
  • Language(s) of instruction
    English
    French
Présentation
Objectives

Computer systems are fast evolving toward more efficiency and enhanced functionalities according to three major, interconnected scientific fields:

  • distributed systems, as many applications are deployed spatially over networks to enable ubiquity,
  • high-performance computing to take advantage of most recent parallel computers in scientific computing and data analytics applications,
  • quantum computing to exploit quantum parallelism and obtain otherwise unreachable performance gains.

The QDCS Master’s program will enable you to acquire deep knowledge of these three fields through both advanced theoretical courses and extensive practice of programming techniques.

Distributed systems deal with protocols and algorithms that allow connectivity and efficient functionality for network based systems, like Internet, Cloud, sensor networks, computing clusters, blockchain distributed systems, and even microbiological circuits. For these systems, the challenges include synchronization, security, concurrency and robustness. Similar issues arise in the field of High Performance Computing (HPC) which aims at efficiently solving computationally intensive problems in applied science or artificial intelligence. HPC pushes modern parallel computer architectures to their limits by using various forms of parallelism, data representations and code optimization. So does distributed computing by means of various methods of communication and algorithmics. They thereby draw the frontier between what can be achieved within the realm of classical Computer Science, and what will only be accessible through a new paradigm: that of Quantum Computing. Quantum Computing and Quantum Information feature novel algorithms and protocols, bringing radical performance gains, together with their own set of conceptual and technical challenges.  

Whilst all of these topics will be covered by default, students who want to deepen their knowledge in one of the three strands will be given the flexibility to do so. This will be the case in particular for students joining the program for the second year (M2) of QDCS. Notice that several available options will let you complete your profile e.g. in Machine Learning, Data Sciences, Security and more.

We will also make sure that non French-speaking students are able to follow the QDCS curriculum in optimal conditions by switching lectures to English and/or providing the necessary course material in English. 

A limited number of scholarships towards your living costs are available [1, 2, 3], some with early deadlines.

Location
GIF SUR YVETTE
ORSAY
Course Prerequisites

Four years of studies in Computer Science, typically the M1 of QDCS or MPRI or similar, e.g. in an engineering school. However, students who are in their fourth year of studies in another scientific field (such as Mathematics or Physics), and having strong foundations in Computer Science (algorithms, programming), will also be able to make the best of this second year Master program, especially if targeting its quantum computing component.

Skills
  • Be able to read and understand research articles in the fields of distributed, parallel and quantum computing.

  • Be able to independently conduct research work on a subject related to these fields.

  • Understand the challenges and tendencies of distributed, parallel and quantum computing systems, current and future.

  • Analyze a code's performance and optimize it using advanced high-performance computing techniques.

  • Proficiency in a wide range of parallel programming paradigms including multi-node (MPI), multi-core (OpenMP), etc.

  • Overcome the large-scale computing challenges of the field, both in industry and research, through wide-ranging theoretical knowledge and practical skills.

Post-graduate profile

During the M2 QDCS, students will acquire solid knowledge of the fields of distributed algorithms, parallel computing, quantum computing, but also strong skills in advanced programming, especially for HPC (MPI, OpenMP, etc.). The choice of optional modules will allow them to become familiar with various possible fields of application, such as AI, data sciences or security. It is therefore a broad and complete training course that allows them to both acquire solid theoretical foundations and master their practical implementation. Thus, the course enables rapid integration into the industrial and scientific worlds, by developing the ability to anticipate technological developments.

Career prospects

With the rise of the Cloud, the IoT, the constant development of supercomputers, the deployment of the European quantum flagship and the national quantum plan, the QDCS Master's program is targeting blossoming scientific and economic fields. The Master enables students to continue with a PhD, by preparing a thesis within a public research organization or the R&D department of a large company. The Master also enables students to easily integrate the industrial world, for example within companies having high computing needs, or more generally in the high-tech world, whether in the R&D departments of large companies or in startups developing cutting-edge software.

Collaboration(s)
Academic partner

École Polytechnique

Télécom Paris

INRIA

Sorbonne Université

Université de Paris

Technion - Israel Institute of Technology

University of Tennessee

Old Dominion University

École Polytechnique Fédérale de Lausanne

Lisbon University

Karlsruhe Institute of Technology

University of Vienna

Laboratories

Formal Methods Laboratory (LMF)
Interdisciplinary Laboratory of Digital Sciences (LISN)
Computer Science Laboratory of the École polytechnique (LIX)
Information Processing and Communication Laboratory (LTCI)
Signals and Systems Laboratory (I2S)

Programme

Here is the list of compulsory courses (of M1 and M2). Accommodations/exceptions are still possible:
- for students who wish to focus on one of the three axes: distributed, hpc or quantum.
- to allow students recruited in Master 2 to follow the Master 1 courses that correspond to their interests.

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[QDCS] Programmation GPU 2.5 12 9
[QDCS] Ordonnancement et systèmes d'exécution 2.5 21
[QDCS] Optimisation stochastique 2.5 21
[QDCS] Initiation au calcul quantique 2.5 21
[QDCS] Frontières du calcul parallèle et distribué 2.5 21
[QDCS] Calcul Haute Performance 2.5 12 9
[QDCS] Big Data 2.5 12 3 8
[QDCS] Auto-stabilisation 2.5 21
[QDCS] Algorithmique parallèle 2.5 12 6 3
[QDCS] Algorithmes de la nature 2.5 21
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[QDCS] Programmation orientée objet 2.5 11 10
[QDCS] Programmation avancée C++ 2.5 9 0 12
[QDCS] Modélisation et optimisation des systèmes discrets 2.5 21
[QDCS] Jeux, apprentissage et optimisation des systèmes complexes 2.5 21
[QDCS] Initiation à l’algorithmique et à la programmation quantique 2.5 21
[QDCS] Algorithmes distribués auto-stabilisants 2.5 21
[QDCS] Algorithmique parallèle 2.5 12 6 3
[QDCS] Algorithmes distribués robustes 2.5 21
[MPRI] Fondements de l'information quantique 2.5 21
[ANO] Programmation MPI 2.5
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
TER Stage 10
Stage long 30
French Language and Culture 2 2 21
French Language and Culture 1 2 30
EIT - Summer School 4
EIT - Innovation and Entrepreneurship Basics 2 3
EIT - Innovation and Entrepreneurship Basics 1 3
EIT - Innovation & Entrepreneurship Study 2 3
EIT - Innovation & Entrepreneurship Study 1 3 21
EIT - Innovation & Entrepreneurship Advanced 2 2.5
EIT - Innovation & Entrepreneurship Advanced 1 2.5 21
EIT - Business Development Lab 2 5
EIT - Business Development Lab 1 4
[SOFT] Soft skills - Transversal Project B 2.5 7 7 7
[SOFT] Soft skills - Transversal Project A 2.5 7 7 7
[SOFT] Soft skills - Summer school 2.5 21
[SOFT] Soft skills - Seminars B 2.5
[SOFT] Soft skills - Seminars (Fairness in Data Science) 2.5 20
[SOFT] Soft skills - 5 Innovation et Entreprenariat avancé 2.5 21
[SOFT] Soft skills - 4 Innovation et Entreprenariat 2.5 21
[SOFT] Soft skills - 3 (Formation à la vie de l'entreprise - Initiation) 2.5 21
[SOFT] Soft skills - 2 (Communication) 2.5 21
[SOFT] Soft skills - 1B (Langue) 2.5 100
[SOFT] Soft skills - 1A (Langue) 2.5 21
[QDCS] Programmation orientée objet 2.5 11 10
[QDCS] Programmation GPU 2.5 12 9
[QDCS] Programmation avancée C++ 2.5 9 0 12
[QDCS] Ordonnancement et systèmes d'exécution 2.5 21
[QDCS] Optimisation stochastique 2.5 21
[QDCS] Modélisation et optimisation des systèmes discrets 2.5 21
[QDCS] Jeux, apprentissage et optimisation des systèmes complexes 2.5 21
[QDCS] Initiation au calcul quantique 2.5 21
[QDCS] Frontières du calcul parallèle et distribué 2.5 21
[QDCS] Calcul Haute Performance 2.5 12 9
[QDCS] Big Data 2.5 12 3 8
[QDCS] Auto-stabilisation 2.5 21
[QDCS] Algorithmique parallèle 2.5 12 6 3
[QDCS] Algorithmes distribués robustes 2.5 21
[QDCS] Algorithmes de la nature 2.5 21
[ISD] Traitement distribué des données. 3 25
[ISD] Traitement automatique des langues 3 25
[ISD] Test et Vérification 3 25
[ISD] Services et applications Web 3 25
[ISD] sécurité 3 25
[ISD] Réseaux sans fil 3 25
[ISD] Réseaux 3 25
[ISD] Représentation des connaissances et visualisation 3 25
[ISD] Rapport d'activité 6 5
[ISD] Projets tuteurés 6 25
[ISD] Projet étude de cas 3 25
[ISD] Programmation système et réseau 3 25
[ISD] Probabilités/Statistiques 3 25
[ISD] Politiques et concepts avancés en sécurité 3 25
[ISD] outils pour la manipulation et l'extraction de données 3 25
[ISD] Optimisation 3 25
[ISD] Modélisation 3 25
[ISD] Modèles Mathématiques 3 25
[ISD] Mémoire 12 8
[ISD] Machine learning/Deep learning 3 25
[ISD] langages Dynamiques 3 25
[ISD] IoT (Internet des objets) 3 25
[ISD] Introduction à l'apprentissage 3 25
[ISD] Extraction et programmation statistique de l'information 3 25
[ISD] Droit informatique 3 25
[ISD] Data Warehouse II 3 25
[ISD] Data Warehouse I 3 25
[ISD] Data Lake 3 25
[ISD] Communication 3 25
[ISD] Cloud Computing 3 25
[ISD] Blockchain 3 25
[ISD] Anglais 3 25
[ISD] Anglais 3 25
[ISD] Algorithmique distribuée 3 25
[ISD] Algorithmique avancée 3 25
[HCI] Virtual Humans : Project 2.5 21
[HCI] Virtual Humans 2.5 21
[HCI] Studio Art Science 2.5 21
[HCI] Serious games : project 2.5
[HCI] Serious games 2.5
[HCI] Programming of Interactive Systems 2 2.5
[HCI] Programming of Interactive Systems 1 2.5
[HCI] Mixed Reality and Tangible Interaction - Project 2.5 21
[HCI] Mixed Reality and Tangible Interaction 2.5 21
[HCI] Interactive Machine Learning : Project 2.5
[HCI] Interactive Machine Learning 2.5
[HCI] Interactive Information Visualization : Project 2.5
[HCI] Interactive Information Visualization 2.5
[HCI] Groupware and Collaborative Work : Project 2.5 21
[HCI] Groupware and Collaborative Work 2.5 21
[HCI] Gestural and Mobile Interaction 2.5
[HCI] Fundamentals of eXtended Reality 2.5
[HCI] Fundamental of situated computing 2.5
[HCI] Fundamental of Human-Computer Interaction 2 2.5
[HCI] Fundamental of Human-Computer Interaction 1 2.5
[HCI] Experimental Design and Analysis 2.5
[HCI] Evaluation of Interactive Systems 2.5
[HCI] Digital fabrication : Project 2.5
[HCI] Digital Fabrication 2.5
[HCI] Design project - Level 2 : Project 2.5 21
[HCI] Design project - Level 2 2.5 21
[HCI] Design project - Level 1 : Project 2.5 21
[HCI] Design project - Level 1 2.5 21
[HCI] Design of Interactive Systems 2.5
[HCI] Creative Design : Project 2.5
[HCI] Creative Design 2.5
[HCI] Career Seminar - Level 2 project 2.5
[HCI] Career Seminar - Level 2 2.5
[HCI] Career Seminar - Level 1 : Project 2.5 21
[HCI] Career Seminar - Level 1 2.5
[HCI] Advanced Programming of Interactive Systems 2 2.5
[HCI] Advanced Programming of Interactive Systems 1 2.5
[HCI] Advanced Immersive Interactions - Project 2.5
[HCI] Advanced Immersive Interactions 2.5 21
[HCI] Advanced Design of Interactive Systems 2.5
[DS] Social and Graph Data Management 2.5 12 9
[DS] Semantic Web and Ontologies 2.5 12 9
[DS] Knowledge Discovery in Graph Data 2.5 12 6 3
[DS] Intelligence Artificielle, Logique et Contraintes : Projet 2.5 10.5 10.5
[DS] Intelligence Artificielle, Logique et Contraintes 2.5 10.5 10.5
[DS] Distributed Systems for Massive Data Management 2.5 12 0 9
[DS] Data Science Project 2.5 3 18
[DS] Bases de données avancées II : Transactions 2.5 9 8 4
[DS] Bases de données avancées I : Optimisation 2.5 9 8 4
[DS] Algorithms for Data Science 2.5 12 9
[ANO] Virtualisation et cloud 2.5
[ANO] Théorie des jeux 2.5 21
[ANO] Tests fonctionnels de protocoles 2.5 21
[ANO] Réseaux sans fil 2.5 21
[ANO] Réseaux mobiles 2.5 21
[ANO] Programmation système et réseaux 2.5 21
[ANO] Programmation MPI 2.5
[ANO] Optimisation multi-objectifs 2.5 21
[ANO] Optimisation discrète non linéaire 2.5 21
[ANO] Optimisation dans les graphes 2.5 21
[ANO] Internet of Things 2.5 21
[ANO] Evaluation de performances 2.5
[ANO] Blockchain 2.5
[AI] TC6: DATACOMP 2 2.5 12 9
[AI] TC5: SIGNAL PROCESSING 2.5 24
[AI] TC4: Probabilistic Generative Models 2.5 16.5 4.5
[AI] TC3: INFORMATION RETRIEVAL 2.5 9 12
[AI] TC2: OPTIMIZATION 2.5 12 4.5 4.5
[AI] TC1: MACHINE LEARNING 2.5 15 6
[AI] TC0 : Introduction to Machine Learning 2.5 15 6
[AI] PRE4: SCIENTIFIC PROGRAMMING 2.5 9 12
[AI] PRE3: DATACOMP 1 2.5 12 9
[AI] PRE2: MATHEMATICS FOR DATA SCIENCE 2.5 12 4.5 4.5
[AI] PRE1: APPLIED STATISTICS 2.5 10.5 10.5
[AI] OPT9: DATA CAMP 2.5 10 15
[AI] OPT8: GAME THEORY 2.5 12 4.5 4.5
[AI] OPT7: ADVANCED OPTIMIZATION 2.5 12 4.5 4.5
[AI] OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING 2.5 21
[AI] OPT5 : VOICE RECOGNITION AND AUTOMATIC LANGUAGE PROCESSING 2.5 21
[AI] OPT4: DEEP LEARNING 2.5 10.5 10.5
[AI] OPT3 : REINFORCEMENT LEARNING 2.5 15 6
[AI] OPT2: IMAGE PROCESSING 2.5 21
[AI] OPT14:MULTILINGUAL NATURAL LANGUAGE PROCESSING 2.5 21
[AI] OPT1 : GRAPHICAL MODELS 2.5 15 6
[AI] OPT 13: Theorie de l'information 2.5 10.5 10.5 0 0
[AI] OPT 12: INFORMATION EXTRACTION FROM DOCUMENTS TO INTERFACES 2.5 10.5 10.5
[AI] OPT 11: DEEP LEARNING FOR NLP 2.5 18 3

To validate the QDCS Master, students must acquire 60 ECTS per year (M1 or M2), for a total of 120 ECTS at the end of the two years (each acquired course gives 2.5 ECTS). To reach this total, a student will have to follow 7 “soft skills” courses (4 in M1 and 3 in M2), as well as courses from the other specializations of the Master in Informatics (see menu below). In addition, in M1, students should accomplish a TER (“Study and Research Work”) project and a short internship (of 1 month). In M2 they will do a long internship (6 months).

Modalités de candidatures
Application period
From 15/03/2021 to 13/06/2021
Compulsory supporting documents
  • Sheet of choice of platform completed to download on the site.

  • Detailed description and hourly volume of courses taken since the beginning of the university program.

  • Curriculum Vitae.

  • Motivation letter.

  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

Additional supporting documents
Contact(s)
Course manager(s)
Administrative office
Admission