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 and Control

Dr Wolfgang Stockinger  
(Imperial College London)

Advanced Topics in Data Science: Deep Learning

 Prof Jared Tanner  
(University of Oxford)

Advanced topics in Stochastic Processes

Dr Oana Lang  
(Imperial College London)

Simulation Methods and Stochastic Algorithms

Prof. Christoph Reisinger
(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