Aller au contenu principal

M2 Quantum and Distributed Computer Science

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

The above list the compulsory courses (of the M1 and M2). Arrangements/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.

To validate the QDCS Master, students must acquire 60 ECTS per year, for a total of 120 ECTS at the end of the two years.

Each acquired course gives 2.5 ECTS. In addition to the compulsory courses, students will 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 the third menu above). During the M1, students will accomplish a TER (“Study and Research Work”, 5 ECTS) project and a short internship (of 1 month, 5 ECTS). During the M2 they will do a long internship (6 months, 30 ECTS).

Modalités de candidatures
Application period
From 15/03/2022 to 13/06/2022
Compulsory supporting documents
  • Curriculum Vitae.

    (A CV detailing all previous studies, internships, trainings, work experience (if any), distinctions and awards, as well as other personal interests and activities.)
  • Motivation letter.

    (A letter detailing the motivation and reasons for willing to study quantum, parallel and distributed computing in the QDCS master's program in the light of previous studies and experiences as well as future career plans.)
  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

    (Grades of all courses since high school.)
  • Sheet of choice of platform completed to download on the site.

    (Please fill out and attach the master's track preference sheet available at: https://master-info-orsay.lri.fr/)
Additional supporting documents
  • The application procedure, which depends on your nationality and your situation is explained here : https://urlz.fr/i3Lo.

    (If you are applying to other masters' programs of Université Paris-Saclay apart from AI,DS,ANO,QDCS,HCI,MPRI tracks of UFR Sciences, please list them in the given sheet.)
  • Detailed description and hourly volume of courses taken since the beginning of the university program.

    (Please provide a document detailing all undergraduate/masters courses you took in the past in case you performed these studies in a language other than French or English, in order to evaluate the adequacy of your past studies.)
  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

    (only needed in case you have officially validated your prior professional experience to count as equivalent to a university degree)
  • Letter of recommendation or internship evaluation.

    (Please attach all recommendation letters from your professors or internship supervisors (if any) in a single PDF file.)
Contact(s)
Course manager(s)
Administrative office
Admission