School of Computer Science and Statistics

School Description:

The School of Computer Science and Statistics has a very active Ph.D. programme, with about 200 students currently enrolled. The objective of the programme is that its Ph.D. students undertake world-class research that will have a demonstrable impact on society at large and, in so doing, to have trained the researchers and academics of the future. 

Current research areas in the School

Computer Science:

Current research in computer science covers a wide range of topics from the theoretical to the applied. Much of this research is funded by the EU, national funding agencies such as Science Foundation Ireland and the Higher Education Authority as well as both indigenous and multinational companies. Staff research interests include: distributed systems including middleware and ubiquitous computing, artificial intelligence, especially logic programming, neural networks and case-based reasoning, cognitive science, computational linguistics, natural language processing, computer vision and robotics, image processing, networks and telecommunications including network management, security, electronic commerce and mobile communications, computer architecture, grid computing, multimedia servers, computer graphics, image synthesis and animation, virtual reality, multimedia systems, information systems and management, management of ICT, health informatics, and formal methods.


The Statistics Discipline has one of the most active research groups in this field in Ireland. The research interests of its staff and graduate students include: modern computationally intensive tools in both Bayesian and classical statistics (techniques which are driven by new applications in science and engineering), theoretical work on modern regression methods, and specialist applications of statistics in business, industry and society. Projects currently supporting research students under funding from national and international agencies include: Bayesian statistical computation using functional approximations like Laplace and variational Bayes, palaeoclimate reconstruction, source separation for multi-spectral astronomical images, estimating species diversity in marine animals, failure and reliability of complex telecommunications networks and optimal road traffic management.


In exceptional circumstances it may be possible to register retrospectively. Applicants wishing to be considered for retrospective admission should contact the Graduate Studies Office by emailing