Course structure

A 4-year PhD programme focused on research.

Year 1

Term 1: Students follow mandatory coursework involving


Foundations: (2 weeks in Oxford, Sept -Oct)

Foundations of Stochastic Analysis

8 hours

Prof. Ben Hambly (University of Oxford)

Foundations of Data Science

16 hours

Prof. Mihai Cucuringu (University of Oxford)

Function Spaces and Distribution Theory

8 hours

Prof. Gui-Qiang G. Chen (University of Oxford)

Tutorials in Stochastic analysis and Data Science



Four advanced core courses in Term 1 at Oxford and Imperial (Oct-Dec):

Advanced Topics in Stochastic Analysis

Dr Camilo Hernández
(Imperial College London)

Advanced Topics in Data Science: Deep Learning

 Prof Jared Tanner  
(University of Oxford)

Advanced topics in Stochastic Processes

Prof. Xue-Mei Li  
(Imperial College London)

Simulation Methods and Stochastic Algorithms

Prof. Mike Giles  
(University of Oxford)


Terms 2 and 3: Students follow three elective courses chosen from Oxford or Imperial College London. 

Each student picks a research topic and a supervisor from the Centre's pool of more than 50 faculty members by end of January and begins working on the supervised research project. Students must choose a supervisor from their home institution.

Years 2, 3 and 4 are dedicated to the students' research project, under supervision of the advisor.

Year 2, Term 1: students progress on their research project is assessed through through a formal exam by two faculty members.

Throughout the 4-year period students participate in cohort activities:

 - Monthly CDT seminars/workshops in Oxford and London

 - Annual CDT Spring Retreat with tutorials and industry speakers

 - Annual Summer School in Mathematics of Random Systems