Mathematics of Random Systems: Analysis, Modelling and Algorithms (Centre for Doctoral Training) DPhil

<strong>The information provided on this page was correct at the time of publication (November 2025). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.</strong> <br/><br/>The Centre for Doctoral Training (CDT) in Mathematics of Random Systems is a four-year doctoral programme that offers academically outstanding students training in the areas of probabilistic modelling and stochastic analysis.<br/><br/>You will participate in a comprehensive doctoral training course, which provides solid training in core skills related to probability theory, stochastic modelling, data analysis, stochastic simulation, optimal control and probabilistic algorithms.<br/><br/>Course structure<br/>In your first year, you will follow four courses matching your area of interest, and choose a main research topic and a research supervisor. This research project will then be expected to evolve into your DPhil thesis in years two to four. <br/><br/>Throughout the four years of the course, you will participate in various CDT activities, including CDT social events, seminars, workshops and training in transferrable skills such as communication, ethics and team-working.<br/><br/>The CDT has multiple industry partners in the areas of data analytics and finance who provide funding for DPhil projects linked to their areas of activity. Candidates with an interest in industry-related research projects are encouraged to apply. Industry-funded DPhil projects provide students with the opportunity to actively engage with our industry partners through collaborative research.<br/><br/>The department offers extensive support to students, from skills training and career development sessions to a variety of social events in a welcoming and inclusive atmosphere. You will have the opportunity to interact with fellow students and other members of your research groups, and more widely across the department. The department aims to offer excellent supervision and provide a stimulating research environment.