Autonomous Systems

Degree:
Autonomous Systems (AUS)

ECTS:
120 ECTS

Field of Study:
Computer Science and Information Technology

Duration:
2 years, full-time

Eligibility:
Hold a Backelor of Science or be in the final year of studies of... (read more).

Tuition fees & scholarships:
For EU and non-EU citizens.
More information.

Language of Instruction:
English
More information.
Watch the video.

Partner Universities:
Check our "Mobility Map".

Application period
:
November 2018 - 1 February 2019
15 February 2019 - 15 April 2019

Autonomous Systems (AUS)

Why study Autonomous Systems at EIT Digital?

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.

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.

Who can apply?

If you wish to apply to this programme you must have a Bachelor of Science in, or be in your final year of studies of:

  • Electrical Engineering / Electronics
  • Computer Engineering
  • Computer Science
  • Information Technology
  • Industrial Engineering
  • Students should have basic competence in programming, data structures and algorithms, and mathematics including calculus, algebra, and mathematical statistics.

Kindly note that relevant work experience can compensate a non-strictly matching bachelor degree. Please justify your work experience in your motivation letter or resume. Once your papers are received, the selection committee will make the final decision on whether your bachelors and work experience are sufficient as prerequisites for the track you have applied for.

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 I&E minor please click here.

Where can I study Autonomous Systems?


Click on the image to download a higher resolution version of the map (PDF format).

Mobility map

Please note that both ENTRY and EXIT universities might be subject to changes. Kindly check before applying.

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.

Aalto University (Aalto), Finland

Link to the university: https://www.aalto.fi/
Programme
Contact: Quan Zhou, quan.zhou@aalto.fi  

LIST OF COURSES:

FIRST SEMESTER (31 ECTS)

Compulsory courses (24 ECTS)

LC-xxxx Language course (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)
SCI-E1010 Introduction course for master’s students: academic skills (1 ECTS)

I&E (7 ECTS)

CS-E5120 Introduction to digital business and venturing      (3 ECTS)
CS-E5130 Digital business management (4 ECTS)

SECOND SEMESTER (29 ECTS)
No compulsory major courses

Elective courses (select 12 ECTS)

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-E8123 Networked control systems (5 ECTS)
ELEC-E8126 Robotic manipulation (5 ECTS)
ELEC-E5710 Sensors and measurement methods (5 ECTS)
MS-E2112 Multivariate statistical analysis (5 ECTS)
ELEC-E8127 Special assignment in automation technologies (1-10 ECTS)

I&E (17 ECTS)

TU-E4100 Startup experience (9 ECTS)
CS-E5140 Global business in the digital age (4 ECTS)
CS-E5430 ICT innovation summer school (4 ECTS)

Royal Institute of Technology, Sweden

Link to the university: https://www.kth.se/en
Programme
Contact: ad: Mihhail Matskin, misha@kth.se

LIST OF COURSES:

FIRST SEMESTER

Compulsory courses (30 ECTS)

DD2410 Introduction to robotics (7.5 ECTS)
DD2421 Machine learning (7.5 ECTS)
ID2209 Distributed artificial intelligence and intelligent agents (7.5 ECTS)
II2202 Research methodology and scientific writing (7.5 ECTS)

I&E (6 ECTS)

ME2072 Entrepreneurship for engineers (6 ECTS)

SECOND SEMESTER
No compulsory major courses

I&E (13 ECTS)

ME2073 Business development lab of entrepreneurship engineers (9 ECTS)
ME2078 Summer course – entrepreneurship for engineers (4 ECTS)

Elective courses

DD2423 Image analysis and computer vision (7.5 ECTS)
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)

University of Berlin (TUB), Germany

Link to the Universityhttps://www.tu-berlin.de/menue/home/
Programme
Contact: Prof Dr Sahin Albayrak; Sahin.Albayrak@dai-labor.de , Prof Stefan Fricke

LIST OF COURSES:
FIRST SEMESTER
Compulsory courses (18 ECTS)

40686 Robotics (6 ECTS)
40548 Machine intelligence I (6 ECTS)
40099 Discrete event systems (6 ECTS)

I&E

70160 Innovation management & entrepreneurship basics (6 ECTS)
I&E elective course (6 ECTS)

SECOND SEMESTER
Compulsory courses (6 ECTS)

40493 Hybrid systems (6 ECTS)

I&E (9 ECTS)

70057 Venture campus - ICT innovation (9 ECTS)

Elective courses

40889 Applied artificial intelligence project (9 ECTS)
40305 Applications of robotics and autonomous systems (9 ECTS)
40346 Autonomous communications (9 ECTS)
40317 Fundamentals of multi-agent technologies (6 ECTS)
40440 Embedded operating systems (6 ECTS)
40441 Embedded systems security lab (6 ECTS)
40335 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 (IIoT) (6 ECTS)

University of Trento (UNITN), Italy

Link to the university: https://www.unitn.it/
Programme
Contact: Daniele Fontanelli; daniele.fontanelli@unitn.it

LIST OF COURSES:
FIRST SEMESTER (27 ECTS)

Compulsory courses (15 ECTS)

140506 Robotic perception and action (9 ECTS)
140440 Industrial robotics (9 ECTS)

Elective courses (select 6 ECTS)

140466 Computational methods for mechatronics (6 ECTS)
145458 Laboratory of applied robotics (6 ECTS)
140472 Distributed systems for measurement and automation (6 ECTS)
145062 Machine learning (6 ECTS)
145943 Wireless mesh and vehicular networks (6 ECTS)
145071 Real time operating systems and middleware (6 ECTS)

I&E (6 ECTS)

Logistics and plants management (6 ECTS)

SECOND SEMESTER

Compulsory courses (9 ECTS)

140469 Modeling and simulation of mechatronics systems (9 ECTS)

Elective courses (select 6 ECTS)

Automatic control (6 ECTS)
145478 Operations research (6 ECTS)
145617 Distributed systems I (6 ECTS)
145764 Deep learning (6 ECTS)

I&E (18 ECTS)

145288 Business development laboratory (9 ECTS)
145455 ICT innovation (5 ECTS)
Summer school (4 ECTS)

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)
LTE 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.

Aalto University (Aalto), Finland

Link to the university: https://www.aalto.fi/
Programme:
Contact: Quan Zhou, quan.zhou@aalto.fi

Specialisation: Robotics and Artificial Intelligence

Aalto University offers a specialization 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.

LIST OF COURSES
Compulsory courses (4 ECTS)

SCI-E1010 Introduction course for master’s students: career and working like skills (1 ECTS)
LC-xxxx Language course (3 ECTS)

Elective courses (select 20 ECTS)

ELEC-E8104 Stochastic models and estimation (5 ECTS)
ELEC-E8116 Model-based control systems (5 ECTS)
ELEC-E8125 Reinforcement learning (5 ECTS)
ELEC-E7120 Wireless systems (5 ECTS)
CS-C3180 Software design and modelling (5 ECTS)
CS-E4850 Computer vision (5 ECTS)
CS-E4890 Deep learning (5 ECTS)
CS-E4830 Kernel methods in machine learning (5 ECTS)
CS-E5710 Bayesian data analysis (5 ECTS)

I&E (6 ECTS)

CS-E5425 I&E study project (6 ECTS)

Master's thesis (30 ECTS)

Quan 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 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.

Royal Instutite of Technology (KTH), Sweden

Link to the university: https://www.kth.se/en
Programme
Contact: Mihhail Matskin, misha@kth.se

Specialisation: Intelligent Autonomous Systems
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.

LIST OF COURSES
Compulsory courses (7.5 ECTS)

II2202 Research methodology and scientific writing (7.5 ECTS)

Elective courses (select 15 ECTS)

ID2223 Scalable machine learning and deep learning (7.5 ECTS)
ID2209 Distributed artificial intelligence and intelligent agents (7.5 ECTS)
EL2320 Applied estimation (7.5 ECTS)
EL2820 Modelling of dynamical systems (7.5 ECTS)
IL2206 Embedded systems (7.5 ECTS)

I&E (12 ECTS)

ME2096 Innovation study project (6 ECTS)
ME2082 Minor thesis project with entrepreneurship (6 ECTS)

Master’s thesis (30 ECTS)

Prof. 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.

University of Berlin (TUB), Germany

Link to the university: https://www.tu-berlin.de/menue/home/parameter/en/
Programme
Contact: Prof Dr Sahin Albayrak; Sahin.Albayrak@dai-labor.de

Specialisation: Applications of Autonomous Systems
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.

LIST OF COURSES:
Compulsory courses (6 ECTS)

40317 Fundamentals of multi-agent technologies (6 ECTS)

Elective courses (select 18 ECTS)

40889 Applied artificial intelligence project (9 ECTS)
40689 Robotics: project (9 ECTS)
40346 Autonomous communications (9 ECTS)
40305 Applications of robotics and autonomous systems (9 ECTS)
40713 Software security (6 ECTS)
40718 Special topics in communications networks and autonomous security (3 ECTS)
40440 Embedded operating systems (6 ECTS)
40441 Embedded systems security lab (6 ECTS)
40621 Optical remote sensing (6 ECTS)
40304 Ad-hoc and sensor networks (6 ECTS)

I&E (6 ECTS)

70151 I&E study (6 ECTS)

Master's thesis (30 ECTS)

Prof 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 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 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 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.

University of Trento (UNITN), Italy

Link to the university: https://www.unitn.it/en
Programme
Contact: Daniele Fontanelli, daniele.fontanelli@unitn.it

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, Industrial Robotics, Real time operating systems 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.

LIST OF COURSES

Compulsory courses (18 ECTS)

140472 Distributed systems for measurement and automation (6 ECTS)
145458 Laboratory of applied robotics (6 ECTS)
Dynamics and control of vehicles and robots (6 ECTS)

Elective courses (select 12 ECTS)

145071 Real time operating systems and middleware (6 ECTS)
140440 Industrial robotics (6 ECTS)
145943 Wireless mesh and vehicular networks (6 ECTS)
140474 Computer vision (6 ECTS)

I&E (6 ECTS)

145623 Innovation and entrepreneurship studies in ICT (6 ECTS)

Master’s thesis (24 ECTS)

Daniele 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 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.

Daniele Bortoluzzi received the MS in Mechanical Engineering in 1998 from the University of Padova and the PhD degree in Mechanics of Machines in 2001 from the University of Brescia. He is associate professor of Mechanics of Machines at the Department of Industrial Engineering at the University of Trento. His research topics are modeling and identification of dynamical systems, with particular application to the qualification of mechanisms for space applications.

EURECOM, France

Link to the university: http://www.eurecom.fr/en
Programme
Contact: Raphaël Troncy; master-eit-aus@eurecom.fr

Specialisation: Sensing, Communicating and Processing Big Data for Autonomous System
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.

LIST OF COURSES
Compulsory courses (16 ECTS)

MOBMOD Mobility modeling (3 ECTS)
Language course (1 ECTS)
Semester project work (7 ECTS)

Select one between (5 ECTS):

MALIS Machine learning and intelligent systems (5 ECTS)
MOBSYS Mobile communication systems (5 ECTS)

Elective courses (select 18 ECTS)

ADST Advanced data science topics (3 ECTS)
CLOUDS Distributed systems and cloud computing (5 ECTS)
COMPARCH Computer architecture (5 ECTS)
EMSIM Emulation and simulation methodologies (5 ECTS)
MOBSERV Mobile application and services (5 ECTS)
SOFTDEV Software development methodologies (5 ECTS)
UMLEMB UML for embedded systems (3 ECTS)
STAND Standardization activities (3 ECTS)
DBSys Database Management System Implementation (5 ECTS)

I&E (6 ECTS)

I&E study (6 ECTS)

Master’s thesis (30 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

Raphaël 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.

Jérôme 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 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.

Eötvös Loránd University (ELTE), Hungary

Link to the university: https://www.inf.elte.hu/en/
Programme
Contact: Zoltan Istenes, zoltan.istenes@eitdigital.eu

Specialisation: Computer Science for Autonomous Driving
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.

LIST OF COURSES
Compulsory courses (10 ECTS)

IPM-18AUTISPE Image and video processing lecture (3 ECTS)
IPM-18AUTISPG Image and video processing practice (2 ECTS)
IPM-18AUTADLEG Applied deep learning (5 ECTS)

Elective courses (select 14 ECTS)

IPM-18AUTSSFEG 3D sensing and sensor fusion (5 ECTS)
IPM-18AUTESASEG Security of autonomous systems (5 ECTS)
IPM-18AUTDAAEG Design and analysis of algorithms (5 ECTS)
IPM-18AUTSTEG Software technology (5 ECTS)
IPM-18feszLAB1 Software technology lab (4 ECTS)
IPM-18AUTPHFTG Human factors in traffic environment (2 ECTS)
IPM-18AUTLFADE Legal framework of autonomous vehicles (2 ECTS)
IPM-18AUTESISE Spatial information systems (2 ECTS)

I&E (6 ECTS)

IPM-13feszSTG_16 I&E study (6 ECTS)

Master’s thesis (30 ECTS)

Zoltán Istenes is an Associate Professor from the Eötvös Loránd University (ELTE), Faculty of Informatics, (Department of Software Technology and Methodology). His research interests include IoT, Robotics, Drones, Self-driving cars, Formal methods. Responsible for the Faculty’s Robotics laboratory, founder and leader of the John von Neumann Computer Society Robotic section. He participated and managed several national and international research and educational projects. He is the academic coordinator of the EIT Digital Master Programs at Budapest. He coordinates the Autonomous Systems at ELTE. He is responsible for the EIT Digital Doctoral School at Budapest.

Dmitrij 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 Lőrincz, CSc, 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 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 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 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.

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.


Do you have questions about the technical content of the programme?

Contact the Programme lead: Prof Daniele Fontanelli; daniele.fontanelli@unitn.it

Are you interested in this programme?

Any other questions?

Degree:
Autonomous Systems (AUS)
ECTS:
120 ECTS
Field of Study:
Computer Science and Information Technology
Duration:
2 years, full-time
Eligibility:
Hold a Backelor of Science or be in the final year of studies of... (read more).
Tuition fees & scholarships:
For EU and non-EU citizens.
More information.
Language of Instruction:
English
More information.
Watch the video.
Partner Universities:
Check our "Mobility Map".
Application period:
November 2018 - 1 February 2019
15 February 2019 - 15 April 2019

© 2010-2018 EIT Digital IVZW. All rights reserved. Legal notice