This is a potential research degree area, subject to the approval of the University. If you are interested in undertaking a research degree in this area, please make contact with the Dean to discuss your proposal.Data Science and Systems Modelling at Abertay covers a wide range of areas at the interface between computer games and complex systems, including in physics, healthcare and the environment.Our overarching agenda is the construction of interactive, real-time and playable simulations in both entertainment and non-entertainment contexts that bring to life systems models through simulation, i.e., digital twins. This playability allows domain experts to investigate complex systems in an intuitive way and supports discovery of new insights. These simulations are underpinned by data-driven models, including AI, to make sense of large real-world data sets. The opportunities at the games-complexity interface are growing, for example how AI and neural networks can generate content for games, and how data analytics can improve the player experience, and research students working in this area have the potential to generate impact on challenging real-world problems.Research in this field draws upon supervisory expertise from within the School of Design and Informatics, including academics with track record in mathematics, physics, artificial intelligence, games programming, and games industry experience.Our research at Abertay is inherently cross-disciplinary and inter-disciplinary, which is of particular importance for research in immersive experiences. We are the lead partner for InGAME: Innovation for Games and Media Enterprise, an AHRC-funded R&D centre for Dundee’s games cluster. Postgraduate students at the University will have access to research and development opportunities, as well as contact with professionals working within the games industry.Undertaking a research degree in Data Science and Systems Modelling at Abertay will prepare you for a career not only in academia but also within wider society, working in complex problem spaces and using modelling and visualisation to unpack that complexity.