The courses and projects in NDS prepare graduates for success by providing comprehensive knowledge of the discipline while maintaining the essential qualities of personal and professional success.
The courses give you a broad foundation, within subjects such as:
In the projects, you will work in-depth with problems of your own choice, often in close cooperation with researchers and/or industrial partners.
You will get familiar with problem based learning, and in addition to the technical content, you will also learn about project management and planning. Moreover, we focus on systems design, where you will design a distributed system to solve a real existing problem. Part of the system will also be implemented and tested.
The master's programme in Network and Distributed Systems covers various topics which qualifies you for a wide range of jobs in top companies all over the world.
Career opportunities include:
Human resource managers are attracted by applicants with international experience and applicants with team-working skills. Previous graduates are now employed at companies such as:
Networks and Distributed Systems at Aalborg University is a two-year master's programme (120 ECTS). Students can also choose to pursue one or two semesters. The programme has strong focus on problem-based learning. The courses and projects are primarily aimed at gaining knowledge on applying methods: analytical, numerical and experimental for analysing, designing and testing of networks and distributed systems.
Each semester has a theme which is supported by the offered courses and the student project.
Communication Networks and Ambient Intelligence (elective)
This course covers the techniques needed to understand and analyse modern data communication networks, Analysis and design of communication networks; network architectures and related Internet protocols. Ambient intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. This course also provides an overview of Human-Computer Interaction in which people are surrounded by intelligent and intuitive interfaces embedded in everyday objects around them.
The workload is designed in such a way as to properly combine the theoretical knowledge gained with practical hands-on experience. You will be required to work on mini projects in groups as part of the course. The projects are designed so that you may gain skills on network models and architectures, selected technologies and tools relevant for monitoring, simulation and emulation.
This course introduces you to stochastic processes starting with definitions of a stochastic process, White-Sense Stationary (WSS) processes, Auto Regressive Moving Average (ARMA) processes, Markov models and Poisson point processes.
The course will be accompanied by mini projects to properly combine the theoretical knowledge gained with practical hands-on experience. You will learn how to simulate stochastic processes through the courses and the mini projects. You will gain the appropriate engineering intuition of the basic concepts and results related to stochastic processes that allow – for a particular engineering problem involving randomness – to design an appropriate model, derive solutions, assess the performance of these solutions and possibly modify the model.
Distributed Real Time Systems
This course is aimed at explaining how and why distributed systems work. You can find such systems for example in cars, production lines or even whole factories where different sensors and controllers need to exchange information in real time. In order to do this, there are several specialised types of networks, or buses as they are called. These will be explained in detail during the course, and you will be provided with the theoretical tools needed to analyse the system off-line. In addition, you will be presented with a set of computer simulators which will help you design and manage a distributed real time network. You will also learn about reliability concepts.
Wireless PHY/MAC fundamentals
A course description will soon be posted.
Course descriptions for the 2nd semester are pending.
Systems of Systems / Complex Systems
This course introduces you to methodologies for designing a system of systems in terms of designing the properties of the individual systems as well as their interconnecting behaviour, establishing the system of systems. A systematic approach to the design of network architectures and local behaviour rules which together constitute systems of systems that are optimal with respect to objectives formulated at a macroscopic level will be presented.
Through mini projects, the course will give an introduction to the opportunities that complex systems provide in research and in applications. Several approaches to the study of complex systems will be described, basic concepts will be introduced and implications for the study of different systems will be discussed.
Machine Learning (elective)
The course gives a comprehensive introduction to machine learning which is a field concerned with learning from examples and has roots in computer science, statistics and pattern recognition. The objective is realised by presenting methods and tools proven valuable and by addressing specific application problems.
Projects within the course will enable you to apply the taught methods to solve concrete engineering problems and will give you competencies in analysing a given problem and identifying appropriate machine learning methods to solve the problem.
Non-Linear Control Systems (elective)
The course comprises an introduction to nonlinear control systems, and it discusses the notions of stability such as stability in Lyapunov sense, asymptotic, and exponential stability. Moreover, the course puts forward tests for checking if a system is stable based on behaviour of a so-called Lyapunov function.
The focus of the course and projects within this course is on geometric methods: observability and controllability tests based on Lie algebras and feedback linearisation. Feedback linearisation is a pure geometrical method that helps to find a certain map which translates a nonlinear system into a linear one. The course introduces nonlinear techniques within observer design and sensor fusion as an extended Kalman filter, an unscented Kalman filter and particle filters. Last but not least, the elements of hybrid control will be introduced; herein, the notion of a hybrid automaton, bisimulation, formal verification of control and hybrid systems, stability and control of switched systems.
Project example, 3rd semester: “Performance analysis and network planning” The project work was based on an existing information processing problem where a distributed system and/or communication system comprises a part of the solution. An initial design may be conducted and made subject to analysis or analysis may be performed on an existing design. Emphasis may be put solely on the communication facility and the associated network planning. Choice of parameters, methods and tools for the analysis must be chosen depending on the problem to be solved.
Project example, 3rd semester: “Enhanced Relaying in 802.11 Wi-Fi Networks via Positioning The topic of this project was to investigate how positioning information can be exploited in the process of selecting relaying nodes in 802.11-based wireless networks. This project was be part of a large research project currently running in partnership with AAU and several other European entities. The investigation of how positioning information can be used to enhance performance in wireless networks is a part of the focus of the newly started EU research project WHERE (Wireless Hybrid Enhanced mobile Radio Estimators) in which AAU is participating.
There are no courses on the 4th semester which is entirely devoted to working on your Master's thesis. Here, you will work on real-life projects and applications. Examples of thesis subjects: Vehicular-networks, Satellite Networks, Fibber to the Home-network Planning, Next Generation Networks, Safety-critical Systems etc.
You can apply until:
Always verify the dates on the programme website.
You only need to take one of these language tests:
The IELTS – or the International English Language Test System – tests your English-language abilities (writing, listening, speaking, and reading) on a scale of 1.00–9.00. The minimum IELTS score requirement refers to which Overall Band Score you received, which is your combined average score. Read more about IELTS.Take IELTS test
The TOEFL – or Test OF English as a Foreign Language – offers a paper-based test (PBT). The final, overall PBT score ranges between 310 and 677, and is based on an average taken from the three test components (listening, structure, and reading). The writing part of this test is scored separately on a scale of 0-6. Read more about TOEFL (PBT).
The TOEFL – or Test Of English as a Foreign Language – offers an internet-based test (iBT). The final, overall iBT score ranges between 0 and 120, and includes a scaled average from the four components (reading, listening, speaking, and writing). Read more about TOEFL (iBT).
The following programmes qualify for the master's in Networks and Distributed Systems:Full bachelors
Applicants with other backgrounds within electrical engineering, computer science, computer engineering or similar will be individually assessed by the Study Board of Electronics and IT.
StudyPortals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
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