Autonomous Systems (AUS)

Why study Autonomous Systems?

For anyone interested in a career related to self-driving cars, robotics or artificial intelligence, EIT Digital Masters School offers a two year master programme in Autonomous Systems (AUS).

AUS is a combination of computer science and electronic engineering. During the programme, students will gain new skills in both areas. In computer science, relevant skills include Internet of Things (IoT), machine learning, artificial intelligence and robot vision. In electronic engineering, relevant fields are automation, control, embedded systems and communications.

Students learn the latest theoretical knowledge and know how to apply their skills in practical real-life problems. Typical application areas of autonomous systems include autonomous vehicles, intelligent robots, industrial IoT and autonomous software systems.

What are the career opportunities?

At the moment, the job market looks very promising. Automotive manufacturing is a major industry in Europe and the university partners are well linked with these companies. In addition, autonomous systems are relevant in service applications and in manufacturing, logistics, shipping, mining, and recycling industries. In addition, autonomous software systems are becoming widespread in application areas, such as media, finance, customer service, and healthcare.

The university partners have an outstanding portfolio of companies in which students are sent for a 6 months internship that concludes their master thesis.

Why choose Autonomous Systems at EIT Digital?

The programme is a double degree programme, which means that you’ll study at two European universities (and countries). The programme has an integrated technical content (Technical major) and business content (Innovation & Entrepreneurship – I&E minor). The I&E minor is shared between all programmes. An integral part of the second year is a Masters thesis (30 ECTS).

The double-degree programme is implemented jointly by leading European universities. The students can select their favorite first (entry) and second (exit) year universities. Each university has a unique specialisation area, which allows students to select their own second year expert area.

How is the programme structured?

All EIT Digital Master School programmes follow the same scheme:

  • Students study one year at an ‘entry’ university and one year at an ‘exit’ university in two of EIT Digital’s hot spots around Europe.
  • Upon completion, graduates receive degrees from the two universities and a certificate awarded by the European Institute of Innovation and Technology.
  • The first year is similar at all entry points with basic courses to lay the foundation for the chosen technical programme focus. Some elective courses may also be chosen. At the same time, students are introduced to business and management. During the second semester, a design project is combined with business development exercises. These teach how to turn technology into business and how to present a convincing business plan.
  • In between the first year and the second year, a summer school addresses business opportunities within a socially relevant theme.
  • The second year offers a specialisation and a graduation project. The gradation project includes an internship at a company or a research institute and results in a Master thesis with a strong innovation and entrepreneurship dimension.

To learn more about the structure of the programme, please click here.

To learn more about the I&E minor please click here.

Who can apply?

Bachelor’s holders in electrical engineering / electronics, computer engineering, computer science, information technology and industrial engineering.

Where can I study Autonomous Systems?

Entry - 1st year (common courses)

  • Royal Institute of Technology (KTH), Sweden 

  • Aalto University (Aalto), Finland 

  • Technische Universität Berlin (TUB), Germany 

  • University of Trento, (UniTN), Italy 


Exit - 2nd year (specialisation)

  • Robotics and Artificial Intelligence (Aalto)
  • Intelligent Autonomous Systems (KTH)
  • Applications of Autonomous Systems (TU Berlin)
  • Real-time perception, decision and control for autonomous driving (UniTN)
  • Sensing, Communicating and Processing Big Data for Autonomous Systems (EURECOM)
  • Computer Science for Autonomous Driving (ELTE)

What can I study at the entry and exit points?


Entry - 1st year, common courses

The first year of the programme will be offered by Aalto (Helsinki), KTH (Stockholm), TUB (Berlin), and UniTN (Trento), and comprises the following core courses, complemented with the courses of the entrepreneurial minor:

Robotics
This course teaches how to mathematically model and control (move) a robotic manipulator and related programming techniques. The students will also learn the basics of modelling and control techniques of mobile robots.

Artificial intelligence (or machine learning)
The course teaches basic principles needed to understand and apply machine learning models and methods, including both supervised and unsupervised learning. After the course, the student will be able to apply the basic machine learning methods to data and to understand new models based on these principles.

Modelling
The course teaches basic modeling methods, including first principle modeling and data-driven modeling, for both static and dynamic systems. After completing the course, a student will be able to select proper modeling approaches for specific practical problems, formulate mathematical models of physical systems, construct models of systems using modeling tools such as MATLAB and Simulink, and estimate the parameters of systems from measurement data.

Estimation
The course teaches both estimation of static systems and state estimation in linear/nonlinear dynamic systems. The student will understand the main concepts in stochastics, estimation and state estimation, the role of uncertainty and is able to implement state estimator in both linear and nonlinear cases.

Control
The course teaches the principles of discrete-time control, including basic concept, discretization, properties of discrete time systems, controller design and performance analysis. After completing the course, the student will understand the principles of discrete-time modelling and control, and design, simulate and implement discrete-time controllers.

Exit - 2nd year, specialisation 


The Programme offers six specialisations that focus on different aspects of intelligent autonomous systems including robotics, machine learning, applications, self-driving vehicles, big data, and computer science. The specialisations are:

Robotics and Artificial Intelligence (Aalto - Helsinki)
Aalto offers a specialisation in robotics and artificial intelligence, with courses in specific robotics topics, vision, embedded systems, and artificial intelligence related subjects.

Intelligent Autonomous Systems (KTH - Stockholm)
KTH offers a specialisation in intelligent autonomous systems with courses in distributed artificial intelligence and intelligent agents, machine learning, scalable machine learning and deep learning, and deep learning in data science as well as courses in robotics and software engineering for data intensive systems.

Applications of Autonomous Systems (TU Berlin)
TU Berlin offers a specialisation in applications of autonomous systems with courses in the fundamentals and applications of multi-agent technologies, robotics and autonomous systems (RAS), machine learning, control systems, autonomous security and networks.

Real-time perception, decision and control for autonomous driving (UniTN - Trento)
UniTN offers a specialisation on real-time perception, decision and control for autonomous driving, with courses in dynamic autonomous vehicles, planning, distributed measurements, computer vision, real-time operating systems, embedded systems and vehicular networks.

Sensing, Communicating and Processing Big Data for IoT (EURECOM - Sophia Antipolis)
EURECOM offers a specialisation in Sensing, Communicating and Processing Big Data for IoT.

Computer Science for Autonomous Driving (ELTE)
ELTE offers a specialisation in computer science for autonomous driving with courses on software technology, artificial intelligence, deep learning, cognitive robotic systems, computer perception, 3D vision, image and video analysis, sensor data aggregation and cyber security.


Entry - 1st year, common courses

Aalto University (Aalto)

First semester

Compulsory major courses (23 ECTS)
Code Course name Credits
LC-xxxx Language course: Compulsory degree requirement, both oral and written requirements 3 ECTS
ELEC-C1320 Robotics 5 ECTS
ELEC-E8101 Digital and Optimal Control 5 ECTS
ELEC-E8103 Modelling, Estimation and Dynamic Systems 5 ECTS
CS-E3210 Machine Learning: Basic Principles 5 ECTS
Compulsory I&E Courses (7 ECTS)
Code Course name Credits
Introduction to IT Business and Venturing 2 ECTS
Management of a Technology Venture 5 ECTS

Second semester

No compulsory major courses

Compulsory I&E Courses (17 ECTS)
Code Course name Credits
Startup Experience 9 ECTS
Growth and Internationalization of Technology SMEs 4 ECTS
ICT Innovation Summer School 4 ECTS
Optional major courses (select 13 ECTS)
Code Course name Credits
ELEC-E8105 Non-linear filtering and parameter estimation 5 ECTS
ELEC-E8111 Autonomous Mobile Robots 5 ECTS
ELEC-E8115 Micro and Nano Robotics 5 ECTS
ELEC-E8118 Robotic Vision 5 ECTS
ELEC-E8123 Networked Control Systems 5 ECTS
ELEC-E8408 Embedded Systems Development 5 ECTS
ELEC-E5710 Sensors and Measurement Methods 5 ECTS
MS-E2112 Multivariate Statistical Analysis 5 ECTS

Total for the whole year: 60 ECTS

Royal Institute of Technology (KTH)

First semester

Compulsory major courses (30 ECTS)
Code Course name Credits
DD2410 Introduction to Robotics 7.5 ECTS
DD2423 Image Analysis and Computer Vision 7.5 ECTS
DD2421 Machine Learning 7.5 ECTS
ID2209 Distributed AI and Intelligent Agents 7.5 ECTS
Compulsory I&E Courses (7 ECTS)
  Course name  
I&E Basics  
Elective I&E  

Second semester

No compulsory major courses

Compulsory I&E Courses (17 ECTS)
  Course name  
Bus Dev Lab  
Summer School  
Optional major courses
Code Course name Credits
EL2450 Hybrid and Embedded Control Systems 7.5 ECTS
DD2424 Deep Learning in Data Science 7.5 ECTS
EQ2321 Speech and Audio Processing 7.5 ECTS
DD2380 Artificial Intelligence 6 ECTS
EQ2871 Cyber-Physical Networking 7.5 ECTS
EL1010 Automatic Control, General Course 6 ECTS
IL2206 Embedded Systems 7.5 ECTS
II2302 Sensor Based Systems 7.5 ECTS
EL2520 Control Theory and Practice, Advanced Course 7.5 ECTS
EQ2425 Analysis and Search of Visual Data 7.5 ECTS
EQ2341 Pattern Recognition and Machine Learning 7.5 ECTS

Total for the whole year: 60 ECTS

Technical University Berlin (TUB)

First semester

Compulsory major courses (18 ECTS)
Code Course name Credits
40686 Robotics 6 ECTS
40548 Machine Intelligence I 6 ECTS
40099 Discrete Event Systems 6 ECTS
Compulsory I&E Courses (7 ECTS)
  Course name  
I&E Basics  
Elective I&E  

Second semester

Compulsory Major Courses (6 ECTS)
Code Course name Credits
40493 Bus Dev Lab 6 ECTS
Compulsory I&E Courses (17 ECTS)
  Course name  
Bus Dev Lab  
Summer School  
Optional major courses
Code Course name Credits
40073 Application System Project 9 ECTS
40305 Applications of Robotics and Autonomous Systems 9 ECTS
40346 Autonomous Communications 9 ECTS
40393 Fundamentals of Multi-Agent Technologies 6 ECTS
40440 Embedded Operating Systems 6 ECTS
40441 Embedded Systems Security Lab 6 ECTS
40326 Applied Embedded Systems Project 6 ECTS
40549 Machine Intelligence II 6 ECTS
40635 Machine Learning Lab Course 9 ECTS
40687 Robotics: Advanced 6 ECTS
40188 Nonlinear Control Systems 6 ECTS
40713 Software Security 6 ECTS
40718 Special Topics in Communications Networks and Autonomous Security 3 ECTS
40282 Introduction to Computer Vision 6 ECTS
40801 Industrial Internet of Things 6 ECTS

Total for the whole year: 60 ECTS

University of Trento (UNITN)

For more information about the programme at UniTN, click here.

First semester

Compulsory major courses (15 ECTS)
Code Course name Credits
140470 Robotic Perception and Action 9 ECTS
140440 Industrial robotics 6 ECTS
Compulsory I&E Courses (7 ECTS)
  Course name  
I&E Basics  
Elective I&E  

Second semester

Compulsory major courses (9 ECTS)
Code Course name Credits
140469 Modeling and simulation of mechatronic systems 9 ECTS
Compulsory I&E Courses (17 ECTS)
  Course name  
Bus Dev Lab  
Summer School  
Optional major courses (12 ECTS)
Code Course name Credits
140466 Computational Methods for Mechatronics 6 ECTS
145062 Machine learning 6 ECTS
140474 Computer vision 6 ECTS
145071 Real time operating systems and middleware 6 ECTS
140472 Distributed Systems for Measurement and Automation 6 ECTS
145458 Planning 6 ECTS
140500 Automatic Control 6 ECTS
145478 Operations research 6 ECTS

Total for the whole year: 60 ECTS

Exit - 2nd year, specialisation

Robotics and Artificial Intelligence at Aalto

Aalto University offers specialisation in robotics and artificial intelligence. The courses combine both electrical engineering and computer science. Elective courses include autonomous mobile robots, micro- and nano robotics, computer vision, robotic vision, and machine learning. The students will learn how to build autonomous, intelligent robots and robotic systems.

The specialisation is offered by the Department of Electrical Engineering and Automation. The department combines expertise from microsystems, electrical engineering and automation. One of the four focus areas is control, robotics and autonomous systems. The total size of the staff is 180, including 16 tenured professors.

The Intelligent Robotics group performs research in robotics, computer vision and machine learning. Future applications of robotics require the systems to operate in complex and unstructured environments such as homes. To operate in environments built for humans, robots need to possess human-like capabilities in areas such as perception, manipulation, and reasoning. The Intelligent Robotics group works actively to develop intelligent robotic systems and robotic vision with a particular emphasis on developing methods and systems that cope with imperfect knowledge and uncertain senses.

The Micro- and nanorobotics group is actively working on micro- and nanorobotic manipulation and automation methods, including acoustic manipulation, microassembly, magnetic micromanipulation, nanoforce characterisation, autonomous micromanipulation, and their applications in biomedical, material and industrial applications. The research work impacts a wide range of applications, including instruments for cancer research, diseases diagnosis, material testing and characterization, and as tools in manufacturing and quality control of soft materials, lab-on-chip devices, integrated circuits and optoelectronics.

Programme coordinator: Zhou Quan, quan.zhou@aalto.fi

Specialisation Mandatory Courses (20 ECTS):

  • Autonomous Mobile Robots (5 ECTS)
  • Micro- and Nano Robotics (5 ECTS)
  • Robotics: Manipulation, Decision Making and Learning (5 ECTS)
  • Robotic Vision (5 ECTS) Specialisation Electives (40 ECTS):
  • Artificial Intelligence (5 ECTS)
  • Distributed and Intelligent Automation Systems (5 ECTS)
  • Computer Vision (5 ECTS)
  • Mechatronics Basics (5 ECTS)
  • Non-linear filtering and parameter estimation (5 ECTS)
  • Embedded Real-Time Systems (5 ECTS)
  • Project work A - Theory (5 ECTS)
  • Project work B - Practice (5 ECTS)

Quan ZhouQuan Zhou is an Associate Professor at the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Finland, heading the Micro- and Nanorobotics group. His research is highly interdisciplinary, covering micro- and nano-physics, micromechatronics, microfluidics, microrobotics, micromanipulation, and intelligent control. His work has been published in high-impact journals such as Nature Communications, Advanced Materials and Small, as well as journals such as IEEE Transactions on Robotics. He is serving as the topic editor in chief on micro- and nanorobotics for the international journal of advanced robotic systems, and member of editorial board of journal of micro-bio robotics. He was also the coordinator of EU FP7 project FAB2ASM, the first PPP project of the European Economic Recovery Plan.

Ville KyrkiVille Kyrki joined School of Electrical Engineering at Aalto University as an Associate Professor in 2012. He serves as the head of the Intelligent Robotics research group. His research interests lie mainly in intelligent robotic systems and robotic vision with a particular emphasis on developing methods and systems that cope with imperfect knowledge and uncertain senses. His published research covers feature extraction and tracking in computer vision, visual servoing, tactile sensing, robotic grasping, sensor fusion (especially fusion of vision and other senses), planning under uncertainty, and machine learning related to the previous. His research has been published in numerous forums in the area, including IEEE Transactions on Robotics, International Journal of Robotics Research, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Haptics, and IEEE Transactions on Image Processing.

Intelligent Autonomous Systems at KTH

KTH offers a specialisation in intelligent autonomous systems. The specialisation emphasises the combination of fundamental principles of computer science and artificial intelligence with autonomous (software and robotics) systems design. KTH will offer courses in the area of distributed artificial intelligence and intelligent agents, machine learning, scalable machine learning and deep learning, and deep learning in data science as well as courses in robotics and software engineering for data intensive systems. The offered courses provide students with both understanding of intelligent autonomous systems and knowledge of methods for developing such systems.

The specialisation is offered by the School of Electrical Engineering and Computer Science. The school provides a very broad expertise in many areas related to electrical engineering, control, software engineering, artificial intelligence, robotics and computer science. The school employs 188 faculty members including 88 tenure track professors.

Departments who will be involved into teaching in the program include: Computer Science department, Software and Computer Systems department, department of Robotics, department of Automatic Control and department of Communication Networks.

Programme coordinator: Prof Mihhail Matskin, misha@kth.se

Specialisation Mandatory Courses:

  • Research Methodology and Scientific Writing (7.5 ECTS) 

  • Scalable machine learning and deep learning (7.5 ECTS) 

  • Distributed Artificial Intelligence and Intelligent Agents (7.5 ECTS) 


Specialisation Electives:


  • Sensor Based Systems (7.5 ECTS) 

  • Deep Learning in Data Science (7.5 ECTS) 

  • Speech and Audio Processing (7.5 ECTS) 

  • Cyber-Physical Networking (7.5 ECTS) 

  • Embedded Systems (7.5 ECTS) 

  • Image Analysis and Computer Vision (7.5 ECTS) 

  • Analysis and Search of Visual Data (7.5 ECTS) 


Prof. Mihhail MatskinProf. Mihhail Matskin is a local coordinator of the Autonomous Systems programme at KTH. He is holding professorship of software engineering at KTH since 2002. He obtained his PhD degree in computer science from the Institute of Cybernetics of the Estonian Academy of Sciences in 1984. Prof. Matskin is known for his work in distributed artificial intelligence, services composition, semantic Web services, agent technology and multi-agent systems. His current research activity is focused on software for autonomous software systems, data analysis, user profiling and trust. Prof. Matskin built and now coordinates the International master programme "Software Engineering of Distributed Systems" at KTH and coordinates the specialisation Cloud Computing and Services (Data Intensive Computing specialisation) from the EIT Digital Master School programme Cloud Computing and Services.

Applications of Autonomous Systems at TU Berlin

TU Berlin offers a specialisation in applications of autonomous systems with courses on the fundamentals and applications of multi-agent technologies, robotics and autonomous systems (RAS), machine learning, control systems, autonomous security and networks. The focus will be first to give an overall know-how of RAS and specific application areas, as well as the mathematical models and development of the methodologies commonly used in the field. The students will then apply their theory into real or simulated systems, having a chance to work closely with the actual research projects carried out at TUB with external partners. TUB particularly positions itself for preparing students toward gaining hands-on experience on applied autonomous systems acting like a bridge between the research domain and real-world problems. Thanks to its multidisciplinary group of professors and researchers from different backgrounds, the extent of the RAS topics covered at TUB ranges from low-level robotics design, motion control, and applied machine learning to high-level planning for RAS and multi-agent systems. Additionally, the project courses cover a diverse set of application areas, such as autonomous cars, sensor networks for digital streets (infrastructure for connected and autonomous driving), IoT in industry, autonomous smart factories and autonomous drones. After experiencing many applications through project courses, the students will then easily and more decisively perform their master thesis projects as a continuation of their courses in conjunction with the ongoing research projects of the scientific institutions.

Programme coordinator: Prof Dr Sahin Albayrak, Sahin.Albayrak@dai-labor.de

Specialisation Mandatory Courses (6 ECTS):

  • Fundamentals of Multi-Agent Technologies (6 ECTS)

Specialisation Electives (60 ECTS):

  • Applications of Robotics and Autonomous Systems (9 ECTS)
  • Applications of Multi-Agent Systems (9 ECTS)
  • Robotics: Project (9 ECTS)
  • Robotics: Current Topics (3 ECTS)
  • Discrete Event Systems (6ECTS)
  • Hybrid Systems (6ECTS)
  • Autonomous Communications (9 ECTS)
  • Software Security for Autonomous Systems (6 ECTS)
  • Autonomous Security (3 ECTS)

Prof Dr hc Sahin AlbayrakProf Dr hc Sahin Albayrak is the head of the chair Agent Technologies in Business Applications and Telecommunication (AOT) as well as founder and head of the Distributed Artificial Intelligence Laboratory (DAI-Labor) at TU Berlin. He is also the founder of Deutsche Telekom Innovation Laboratories, and the founding director of the Connected Living Association and the German-Turkish Advanced Research Centre for ICT (GT-ARC). His research interests include agent-oriented modeling, multiagent technologies, smart cities, intelligent mobility systems, next generation telecommunication services, smart energy systems, and autonomous security.

Prof Dr Oliver BrockProf Dr Oliver Brock is the Alexander-von-Humboldt Professor of Robotics in the School of Electrical Engineering and Computer Science at the Technische Universität Berlin in Germany. He is the head of the Robotics and Biology Laboratory. His research focuses on mobile manipulation, interactive perception, grasping, manipulation, soft material robotics, interactive machine learning, deep learning, motion generation, and the application of algorithms and concepts from robotics to computational problems in structural molecular biology. He is the president of the Robotics: Science and Systems foundation.

Prof Dr Manfred OpperProf Dr Manfred Opper is a full professor and the head of the research group of Methods of Artificial Intelligence. His research fields include approximate probabilistic inference (applied mainly to Gaussian process models and stochastic dynamical systems), statistical learning theory (using approaches from mathematical statistics, statistical physics, information theory), and statistical physics of complex systems. These can be applied to estimation.

Prof Dr Jörg RaischProf Dr Jörg Raisch holds the chair of Control Systems in the Department of Electrical Engineering and Computer Science at Technische Universität Berlin. He is also an External Scientific Member of the Max Planck Institute for Dynamics of Complex Technical Systems, where he heads the Systems and Control Theory Group. His main research interests are hybrid and hierarchical control, and control of timed discrete event systems in tropical algebras, with applications in chemical, medical and power systems engineering.

Infrastructure

Autonomous systems require an understanding of control, robotics, machine learning, applied AI, multi-agent technologies, IoT, networks and security. The research and development of each of these majors are offered as lab spaces and test beds at TU Berlin, both for the courses and for master thesis projects. In particular, teaching and research in Autonomous Systems is organized by Distributed Artificial Intelligence Lab, Robotics and Biology Lab, Artificial Intelligence Group and Control Systems Group at TU Berlin. Each lab group has close collaborations with other research institutes and industry partners, which gives students and interns the opportunity to be directly involved in projects of practical impact and potential spin-offs. Through these funded research projects there are infrastructures for the use of the students to foster their hands-on experiences on the application of autonomous systems in various fields. In particular, autonomous driving simulator and roadside infrastructure for autonomous vehicles, autonomous drones for transportation and surveillance, assistant robots for HRI at home and industry, autonomous smart factory and IoT for Industry 4.0, robotic platforms for grasping (e.g., Amazon Picking Challenge 2015 Winner) and soft robotics, robocup soccer team, smart home showroom, and the e-mobility & micro smart grid testbed are among the lab infrastructure and environments available for researchers and students at TU Berlin.

Real-time perception, decision and control for autonomous driving at UniTN

UniTN offers a specialisation in real-time perception, decision and control for autonomous driving. The specialisation aims at offering the tools and the theoretical foundations to tackle the new challenges of the next generation of autonomous systems.

Once limited to small and constrained industrial environment, robotic technologies have now become pervasive and are currently one of the main driver of innovation in many areas. Two market segments in which this evolution is more apparent is that of autonomous driving cars and of service robotics. Similarly, the revolution of the Industry 4.0 has changed the landscape also for industrial applications. Modern manufacturing requires unprecedented levels of flexibility, the ability to react to unforeseen events, lean and on-demand production. These changes have put a strain on “classic” technologies, giving birth to a new wave of industrial robots, more intelligent, more flexible and more interconnected than ever.

In order to face the demanding challenges of modern robotics, professionals are required to have a large number of abilities such as can be found in broad and multifaceted curriculum. In this challenging and exciting scenario, UniTN offers an education pathway that rests on four main pillars: mastering optimal control techniques and their applications to automotive and robot systems, applying control and measurement algorithms with a number of heterogenous source of information, developing planning and decision making mechanisms that enable the robot to optimise its operation and react to unforeseen events, develop scalable and robust software architectures that make the most of the existing communication infrastructure. Elective courses include computer vision, embedded systems, real-time operating systems, agent-oriented software engineering and wireless mesh and vehicular networks.

The specialisation is offered by the Department of Industrial Engineering (DII) and the Department of Engineering and Computer Science (DISI), both at the University of Trento. DII provides a very broad expertise in many areas related to industrial engineering, industrial and service robotics, dynamics, modelling and control of vehicles, while DISI offers a wide knowledge on software engineering, artificial intelligence, decision making and computer science. Together, the DII and DISI employs 93 faculty members, about 90 research staff members and more than 300 PhD students.

Programme coordinator: Prof Daniele Fontanelli, daniele.fontanelli@unitn.it

Specialisation Mandatory Courses (18/21 ECTS):

  • Planning (6 ECTS)
  • Distributed systems for measurements and automation (6 ECTS)
  • Dynamics and control of vehicles and robots (6/9 ECTS)

Specialisation Electives (30/33 ECTS):

  • Computer vision (6 ECTS)
  • Embedded systems (6/9 ECTS)
  • Real time operating systems and middleware (6 ECTS)
  • Agent oriented software engineering (6 ECTS)
  • Wireless mesh and vehicular networks (6 ECTS)

Daniele FontanelliDaniele Fontanelli received the MS degree in Information Engineering in 2001, and the PhD degree in Automation, Robotics and Bioengineering in 2006, both from the University of Pisa, Pisa, Italy. He was a Visiting Scientist with the Vision Lab of the University of California at Los Angeles, Los Angeles, US, from 2006 to 2007. From 2007 to 2008, he has been an Associate Researcher with the Interdepartmental Research Center ``E. Piaggio'', University of Pisa. From 2008 to 2013 he joined as an Associate Researcher the Department of Information Engineering and Computer Science and from 2014 the Department of Industrial Engineering, both at the University of Trento, Trento, Italy, where he is now an Assistant Professor. He has authored and co-authored more than 100 scientific papers in peer-reviewed top journals and conference proceedings. His research interests include real-time control and estimation, resource aware control, localisation algorithms, wheeled mobile robots control, and service robotics.

Luigi PalopoliLuigi Palopoli received the PhD degree from “Scuola Superiore S. Anna, Pisa” in 2002. He is associate professor of computer engineering at the “Dipartimento di Ingegneria e Scienza dell’Informazione (DISI)”, University of Trento. His research interest includes real–time embedded control, formal methods and stochastic analysis of real–time systems.

 

 

Sensing, Communicating and Processing Big Data for Autonomous Systems at EURECOM

EURECOM is offering a specialisation in Sensing, Communicating and Processing Big Data for Autonomous Systems. This specialisation aims at describing the methods and tools to process and analyse data from sensors (big data integration, semantic interoperability, data mining and analytics) and at understanding the communication mechanisms adapted to the sensors’ constraints (low energy consumption, limited computing capacities, etc.). Consequently, this master specialisation is at the crossroad between communication systems and data science skills.

Programme coordinator: Raphaël Troncy, master-eit-aus@eurecom.fr

Compulsory Major Courses (8 ECTS)

Select one of the following courses:

Specialisation Electives (8 ECTS):

      Semester Project (7 ECTS):

      Example of recent past semester projects:

        • Flying Robots for Future Wireless Networks Beyond 5G
        • Automated code generation for IoTs
        • Social Mobility Modeling in Urban Environments
        • Self-driving MarioKart with TensorFlow
        • Connected Cars? Implementation of LTE Device-to-Device (LTE D2D) on OAI
        • Guard drone
        • Finding IoT Networks in the Wild
        • Security and Privacy for Internet of Things

          Language course

          • French or another foreign language if the student has already a B2 level in French (1 ECTS)

          Compulsory Innovation and Entrepreneurship courses (6 ECTS)

          • I&E Study (6 ECTS)

          Master’s Thesis (30 ECTS)

          Total for the whole year (60 ECTS)

           

          Raphaël TroncyRaphaël Troncy is an Assistant Professor within the Data Science Department at EURECOM where he teaches classes on "Human-computer interaction for the Web" and on "Semantic Web technologies”. His research interests include Large-scale data integration using Semantic Web technologies, Information Extraction, Natural Language Processing, Semantic Graphs and Web Science. He researches, for example, methods and tools that require semantic data integration and enrichment for smart cities, WoT including connected cars.

          Christian BonnetChristian Bonnet is a Professor from the Communication Systems Department at EURECOM. His research focuses on Protocols for mobility management for all IP based systems, Internet of Things, M2M Communications, Mobile Ad Hoc protocols (routing, multicast, topology management) and Cross Layer design of wireless systems. He coordinates a Master’s degree in Mobile Computing Systems.

          Jérôme HärriJérôme Härri is an Associate Professor from the Communication Systems Department at EURECOM, where he conducts research on wireless vehicular networks. His research interests are related to the optimization of the vehicular wireless channel usage, to the investigation of cooperative intelligent transportation systems strategies, and to the characterization of the mutual relationship between vehicular mobility and vehicular communication. He coordinates a Post Master’s degree in Communications for Intelligent Transport Systems.

          Adlen KsentiniAdlen Ksentini is an Assistant Professor from the Communication Systems Department at EURECOM. His research focuses on mobile and wireless Mobile and wireless Networks; Software Defined Networking (SDN), Network Function Virtualization (NFV), Content Delivery Networks (CDN), and Performances evaluation. He develops Edge Computing to offer new solutions to increase sorting and computing capacities. He coordinates a Master’s degree in Internet of Things.

           

           

          Computer Science for Autonomous Driving at ELTE

          ELTE offers a specialisation in computer science for autonomous driving. The specialisation comprises core courses on the design, implementation, operation and maintenance of software for self-driving vehicles. Students get thorough and up-to-date knowledge in the fields of artificial intelligence and machine learning.

          The specialisation provides comprehensive knowledge of tools and methods for computer perception, especially in 3D vision, image and video analysis, sensor data aggregation. Students can also learn fundamentals and applications of computer graphics and computational geometry. Our courses cover a wide spectrum of topics related specifically to current challenges of autonomous vehicles, and they prepare students for a variety of career options in software development in autonomous systems.

          Elective courses include security of autonomous systems, design and analysis of algorithms, spatial information systems, legal framework for autonomous vehicles. Our vision is an equal emphasis on fundamental principles and practical skills. To this end, we combine the training of students with the research activities at the university, and we invite our best students into R&D&I work of research labs. These long-running, innovative software-engineering projects are carried out in cooperation with industrial partners like Ericsson and Bosch. In this specialisation students can choose lab work as an elective course and work together in international teams on real problems of autonomous systems. ELTE offers students the opportunity to explore and develop their careers through professional practice at partner companies.

          ELTE endeavours to provide a supportive and enjoyable atmosphere for learning - we provide helpdesk and extensive assistance during studies.

          Programme coordinator: Tamás Kozsik, tamas.kozsik@elte.hu

          Specialisation Mandatory Courses (20 ECTS):

          • Software Technology (5 ECTS)
          • Image and Video Analysis (5 ECTS)
          • 3D Sensing and Sensor Fusion (5 ECTS)
          • Applied Deep Learning (5 ECTS)

          Specialisation Electives (4-10 ECTS credits out of 26):

          • Research & Development Lab (5 ECTS)
          • Security of Autonomous Systems (5 ECTS)
          • Design and Analysis of Algorithms (5 ECTS)
          • Cognitive Robotic Systems (5 ECTS)
          • Spatial Information Systems (2 ECTS)
          • Human Factors in Traffic Environment (2 ECTS)
          • Legal Framework of Autonomous Vehicles (2 ECTS) I&E thesis (6 ECTS credits)

          I&E thesis (6 ECTS)

          Dmitrij ChetverikovDmitrij Chetverikov, DSc, Physicist, Computer Scientist, Professor at Department of Algorithms and Application. His main research interests are computer vision, 3D sensing and sensor fusion, image and video processing.

          András LőrinczAndrás Lőrincz, DSc, Physicist, Computer Scientist, Senior researcher at Department of Software Technology and Programming Methodology. His research focuses on distributed intelligent systems and their applications in neurobiological and cognitive modeling, AI driven deep learning, human-computer collaboration, knowledge fusion, common sense reasoning.

          László Zsolt VargaLászló Zsolt Varga, CSc, habil., Computer Scientist, Associate Professor at Department of Software Technology and Programming Methodology. His main interests are distributed systems, AI, multi-agent systems.

          Péter BurcsiPéter Burcsi, PhD, habil., Mathematician, Computer Scientist, Associate Professor at, and Head of Department of Computer Algebra. His main interests are discrete mathematics and its applications (e.g. cryptography and security), and combinatorics.

          László SzabóLászló Szabó, CSc, habil., Teacher of Mathematics & Informatics, Associate Professor at Department of Algorithms and Applications. His main interests are discrete mathematics, discrete and computational geometry, theory of algorithms, database systems.

           

           


          Any questions about the technical content of the programme? Contact the programme lead: Prof Vuorimaa Petri, petri.vuorimaa@aalto.fi

          Interested? Apply now!

          Any other questions? Click on the button 'Contact us to learn more!' on the top right-hand side of the page.

          Are you interested to apply? Check out or application guidelines. Deadlines for a start in September 2018 are February 1 for non-EU citizens and April 15 EU/EEA/CH citizens.

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