Course structure

A 4-year PhD programme focused on research.

Students are enrolled in the CDT for a 4-year period.

Year 1


Term 1: Students follow mandatory coursework involving

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Four 8-hour introductory courses in the first 2 weeks in Oxford (Sept -Oct)

Foundations of Stochastic Analysis

Prof. Ben Hambly (University of Oxford)

Foundations of Data Science

Prof. Mihai Cucuringu (University of Oxford)

Function Spaces and Distribution Theory

Prof. GuiQiang Chen (University of Oxford)

Programming in Python

 

 

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

Advanced Topics in Stochastic Analysis

Dr Andreas Sojmark 
(Imperial College London)

Advanced Topics in Data Science: Deep Learning

 Prof Jared Tanner (University of Oxford)

Stochastic partial differential equations

Dr A Chandra & Dr G Cannizzaro
(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 40 faculty members by end of January and begins working on the supervised research project


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 a Confirmation exam. 

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

 - "Problem-solving" group projects
 

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