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The Faculty of Technology provides access to leading-edge facilities that are continually updated to ensure that your studies are supplemented by industry-standard equipment supported by our highly trained technicians and academics.

Our School of Computer Science and Informatics includes over 20 computing laboratories equipped with machines ranging from HP dual-boot, all-in-one computers in the Computer Science laboratories to ultra high-performance PCs in our Computer Games Programming areas. Student work is stored and backed up on dedicated high performance and there are numerable software packages freely available to students via open source and similar licensing (for example Linux and Java) and all students have access to Microsoft Office.

Students on business computing courses also have access to Digital Simulations; single-player, online experiences that can be used to educate, based around complex scenarios. The HPE IT Service Management (ITSM) Simulation has been designed to show participants how process, technology and communication can work together to improve the delivery of IT to the business.

We believe that computing students can be most creative in front of a computer and we encourage hands-on experience of sophisticated technology during scheduled laboratory classes and through independent study at other times.


Facilities include:




A four-man team, including Professor Neri and Dr Lorenzo Picinali, have developed a way of linking their high-spec multimedia computers to utilise their unused resources, especially at weekends and night-times.

A string of machines with more than 500 computational cores between them in DMU’s Creative Technology Studios have been linked into a cluster, labelled the Memenet.

Controlled by an external computer in the university’s Centre for Computational Intelligence, the cluster has been working in the background creating algorithms (problem-solving programs) without affecting work being done by students.

Professor Neri said, “Part of our project consists of letting the machine design the algorithms itself. The machine analyses the problem and makes the decision. In order to do this we need to run very extensive computational experiments – many algorithms, many times, on many problems.

Other universities have been spending over £1 million on supercomputers, our idea is that we have many little computers and we can distribute our optimisation algorithms over all the available computational cores.”

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