Training the next generation of interdisciplinary experts in Probability, Stochastic Analysis and applications

The Centre for Doctoral Training in Mathematics of Random Systems: Analysis, Modelling and Algorithms is a comprehensive doctoral programme focused on probabilistic modelling, stochastic analysis and their applications based at the Oxford Mathematical Institute, with the ambition of training the next generation of academic and industry experts in stochastic modelling, advanced computational methods and Data Science.
The CDT offers a 4-year comprehensive training programme at the frontier of scientific research in Probability, Stochastic Analysis, Stochastic Modelling, stochastic computational methods and applications in physics, finance, biology, healthcare and data science.
Students receive solid training in core skills related to probability theory, stochastic modelling, data analysis, stochastic simulation, optimal control and probabilistic algorithms. In the first year, students follow four core courses on foundation areas as well as three elective courses, and undertake a supervised  research project, which then evolves into a PhD thesis.
Throughout the four years of the course, students will participate in various CDT activities with their cohort, including a CDT spring retreat, the annual summer school as well as regular seminars, workshops and training in transferrable skills such as communication, ethics and team-working.
The CDT offers opportunities for research in a wide range of subjects, ranging from research questions in fundamental mathematics to cutting-edge industrial applications:
1. Stochastic analysis: foundations and new directions
6. Randomness and universal behaviour in physical systems
2. Stochastic partial differential equations
7. Stochastic modelling and data-driven modelling in finance
3. Random combinatorial structures: trees, graphs, networks, branching processes
8. Mathematical modelling in biology and healthcare
4. Stochastic computational methods and optimal control
9. Mathematical and algorithmic challenges in data science
5. Random dynamical systems and ergodic theory
10. Mean-field models and agent-based modelling


 randomsystemscdt      CDTRandomSys