High-performance computers will become ubiquitous in the coming years. With applications ranging from autonomous vehicles to data centres and the internet of things, the challenge for the industry is to design software and hardware that can keep up with the high demands for power and energy that will follow. Successfully developing and exploiting such systems and bringing them to market will be the key to the success of future computing.

If those challenges sound enticing, and you want to gain the cutting edge skills, in-depth knowledge and methodologies required to face them, then this is the programme for you. The fundamental goal of the education is to investigate how to the needs of future industries, reliant on high computational performance and energy efficiency, can be met. How can systematic methods based on the latest research results in the field of computer systems engineering contribute to these developments?

For this, a holistic view is essential. This could combine, for example, how algorithms specified in a high-level language can best make use of particular computational structures, and how computational resources should best be designed, managed and organised. The programme focuses on hardware-software co-design aspects, to develop domain-specific architectures related to current emerging applications such as AI and deep learning.

The programme is for students who want to lead and participate in software and hardware development, and carry out cutting-edge development work in computer architecture, parallel programming sustainability and energy-efficiency. It is also ideal for those who aim to pursue specialised topics such as parallel and reconfigurable architectures, real-time systems and computer graphics.

The programme also offers the possibility to dive deep into innovation and entrepreneurship, with a special emphasis on how technical ideas can be transformed into viable businesses, either as startups or as part of existing organisations.

In addition to the depth of the compulsory and specialisation courses, students will also have a wealth of elective courses to choose from, increasing your breadth of knowledge into related areas such as computational science and machine learning.