EPSRC Centre for Doctoral Training in Next Generation Statistical Science: the Oxford-Warwick Statistics Programme
The Statistics Department - University of Oxford and the Statistics Department - University of Warwick, supported by the EPSRC, is running a joint Centre of Doctoral Training in the theory, methods and applications of Statistical Science for 21st Century data-intensive environments and large-scale models. This is the first centre of its type in the world and will equip its students to work in an area in growing demand both in academia and industry.
Every academic year, OxWaSP will recruit at least 5 students attached to Warwick and at least 5 attached to Oxford. Last year, 12 students were funded to join the programme. Each student will be funded with a grant for four years of study. Students spend the first year at Oxford developing advanced skills in statistical science. In the first two terms students are given research training through modular courses: Statistical Inference in Complex Models; Multivariate Stochastic Processes; Bayesian Analyses for Complex Structural Information; Machine Learning and Probabilistic Graphical Models; Stochastic Computation for Intractable Inference. In the third term, students carry out two small research projects. At the end of year 1, students begin a three-year research project with a chosen supervisor at their "home" university, Warwick or Oxford.
Training in years 2-4 includes annual retreats, workshops and a research course in machine learning at Amazon (Berlin). There are funded opportunities for students to work with our leading industrial partners and to travel in their third year to an international summer placement in some of the strongest Statistics groups in the USA, Europe and Asia including UC Berkeley, Columbia University, Duke University, University of Washington in Seattle, ETH Zurich and NUS Singapore.
Applications may still be considered if places are available.
For further information on applying to the programme at one or both institutions see: