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Research context and research institute

The master programmes of computing and information sciences are offered in close collaboration with the research institute for Computing and Information Sciences (iCIS) of the Radboud University Nijmegen. The mission of this institute is to improve software development fundamentals by developing formal, mathematically founded theories, methods and tools to support the specification, design, analysis and evaluation of computer-based systems. Research aims include improving the quality of software, with an emphasis on reliability, security, architecture and system alignment.

Research at ICIS is organized in three different research sections: Software Science (SwS), Digital Security (DiS) and Data Science (DaS). The courses of our curricula are taught by staff members of these sections.

The research topic of your master thesis is likely to be linked to or embedded in the research of one of these sections, and you can find a supervisor for master thesis in these different groups. More information about the research of these sections is given below.

For Information Sciences, there are also interesting links with business modelling and business administration. For the new master programme, our institute has established a close collaboration with the Faculty of Management to create further possiblities for students interested in the application of information technology in these areas. For your master's thesis in Information Sciences, you may find a second supervisor from this Faculty.

Software Science (SwS)

Research carried out by the SwS section focuses on the use of formal models in the development of computer systems in relationship to application areas, on the one hand, and on the development of the required basic techniques, on the other hand. Our theories, methods and tools are empirically validated through the development of challenging applications from the industrial, health care, governmental, and financial sectors, thus aiming at bridging the gap between theory and practice. The topics driving the research carried out in the section are models and modeling.

Models provide abstractions of systems, artificial or natural, that allow reasoning about properties of these systems, ignoring extraneous details while focusing on relevant ones. Explicit models have always played a key role in science and engineering. There is now a clear trend in computer and information science towards the systematic use of models as the primary artifacts throughout the engineering life cycle of computer-based systems. Requirements, behavior, functionality, construction and testing strategies of computer-based systems are all described in terms of models. Models are not only used to reason about a system, but also used to allow all stakeholders to participate in the development process and to communicate with each other, to generate implementations, and to facilitate reuse.

Subthemes on which the section's research focuses are:
computer-aided verification and analysis, and model-based testing in relationship to embedded systems; model-based application generation using techniques from functional and generic programming; decision support systems in relationship to applications from health care and industry; model development in collaboration with stakeholders.

For more information on research carried out at the Software Science section, click here.
Digital Security (DiS)

Digital security is an increasingly important issue in our society. The Digital Security group works on a broad range of topics in computer security, including applied cryptography, security protocols, smartcards and RFID, and the security and correctness of software. We are also interested in societal aspects of digital security, such as
privacy and e-voting, and interaction with disciplines outside computer science such as cryptography and law.

For more information on research carried out at the Digital Security section, click here.

Data Science (DaS)

The main challenge of the Data Science section  is to make computer systems more 'intelligent'. We do this by both pursuing the connectionist and the symbolic approach. The connectionist approach follows the statistical view on knowledge; our specific expertise lies in Bayesian methods and machine learning with main applications in bioinformatics and neuroscience. The symbolic approach follows the (formal) logical view on knowledge; our specific expertise lies in type theory and proof assistants with applications in software verification and formalization of mathematics. A central notion from logic towards intelligence is 'reflection': the view on a system (including ourselves) from higher levels. Within this view we perform research on mind-brain-mindfulness in order to contribute to the understanding of what intelligence is.

For more information on research carried out at the Data Science section, click here.