M.Sc. in Parallel and Distributed Computer Systems

Curriculum

Address the most ambitious issues in experimental computer science
The PDCS programme is unique in the Netherlands and one of the few of its kind in the world. It involves the design, implementation, testing and analysis of complex systems, emphasising experimental computer science rather than theory. The curriculum includes several software lab courses, which train you to carry out scientific experiments using methodologies to solve challenging user cases and research problems.

You’ll study the hardware and software architectures that can scale to up to geographically distributed systems, serving billions of applications in parallel or millions of concurrent users. You’ll understand how to address challenges of scalability, performance, reliability, security and privacy. And you’ll learn how to use experimental and empirical approaches to address the enormous complexity of real-world systems and their applications, and how they interact with actual users.

What’s more, you’ll design, implement and evaluate the properties of complex systems. To do this, you’ll work in small teams to learn valuable collaborative research skills. All courses revolve around our research areas:

High Performance Distributed Computing – programming environments for clusters, clouds, heterogeneous systems, many-core, networked systems, sensor networks, smartphones and internet of things, applied to deep learning, astronomy, forensics, e-health, climate, multimedia, bioinformatics, smart cities, etc.

Massivising Computer Systems – making (distributed) computer systems scalable, reliable and performant, yet forming efficient, engineered ecosystems available to massive numbers of end-users. This approach improves large-scale infrastructure, leverages modern hardware such as programmable accelerators and RDMA networking, and uses emerging storage technology – touching datacentres, cloud computing and big data. This affects numerous popular applications in the digital economy, such as e-Science, business-critical applications, online gaming, etc.

Systems for Artificial Intelligence – systems to apply AI algorithms in big-data environments, especially applying deep learning and/or logical reasoning techniques to large volumes of unstructured data (natural language processing) or semi-structured data (knowledge graphs/social networks).

Systems and Network Security – system-level security and reliability, including software hardening, exploitation, binary and malware analysis, software testing, security metrics, side channels, hardware fault attacks and reverse engineering.

The start date of this programme is September 1st.

First year

The Master's in Parallel and Distributed Computer Systems is a two-year programme that offers you a great balance between classroom and applied work. The curriculum is made up of compulsory core courses, elective courses and a Master’s thesis. The emphasis shifts gradually from classroom lectures to independent, research-oriented study with practical work, seminars and the identification of open research questions.

Compulsory courses include:

• Parallel Programming – how to reduce execution time by writing programs that run in parallel on a large number of processors;
• Distributed Systems – software systems that, single or combined, enable distributed operation, from clusters up to global scalability;
• Computer & Network Security – a wide-ranging course on the security of computer systems and networks.

Elective courses you can choose include Evolutionary Computing, Service-Oriented Design, Coding and Cryptography, Information Visualisation and literally dozens of others – the possibilities are endless. You’ll also have the opportunity to test your knowledge in at least one large project before your final thesis.

Second year

In your second year, you’ll take a core course in Advanced Operating Systems, as well as choose three electives. In parallel, you’ll write your research proposal. The final semester is dedicated to the Master’s thesis, which usually involves conducting research together with a member of the academic staff.