Core Modules
Term 1 consists of core training, common to all incoming PhD students, that aims to introduce students to all research areas represented in the department, develop their core skills and build a cohort.
Term 2 consists of four optional core modules, introducing students to a number of important advanced topics in different areas of probability and statistics. Topics are reviewed each year in line with the incoming cohorts research interests. The below list is current for the 2023/2024 intake and will be updated over the summer 2024.
Term 1
Milestones in Probability and Statistics
- This module aims to expose students to the whole breadth of probability and statistics, through the discussion and presentation of seminal papers and their impact on the wider subject.
- Students will be trained in reading research papers, working in groups, and presenting research ideas, whilst developing an appreciation of the importance of cross-fertilisation among different disciplines.
Statistical Frontiers
- This module consists of a series of 1-hour presentations on a number of research topics by relevant academics, aiming to introduce students to the research areas active in the department.
Additional training:
- Research Integrity Training
- The University provides online research integrity training relevant to all those involved in delivering, supervising or supporting research.
- Graduate Teaching Assistant Training
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The teaching on several of the Department’s undergraduate courses is supported by tutorial groups and example classes, many of which are led by research students.
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Term 2
Students will be expected to choose at least two Graduate Topics modules, each module consisting of three 10-hour graduate-level lecture courses, with themes covering the broad spectrum of research interests in the department. Details of topics will be decided in the summer before the cohort arrives, taking into consideration the interests of the incoming cohort.
Examples of the modules and topics in Term 2 of 23/24 include:
Graduate Topics in Applied Probability and Mathematical Finance
- Topic 1 - Levy processes and self-similar Markov processes
- Topic 2 - Jump processes and applications in Finance
- Topic 3 - BSDEs with applications in mathematical finance
Graduate Topics in Computational Stochastics and Machine Learning
- Topic 1 – Simulation and Inference for stochastic processes
- Topic 2 – Sequential Monte Carlo
- Topic 3 – Statistical Foundations of Machine Learning
Graduate Topics in Probability
- Topic 1 – Mixing Times for Markov Processes
- Topic 2 – Introduction to Large Deviations
- Topic 3 – SPDEs
Graduate Topics in Statistics
- Topic 1 – Model Uncertainty and Bayesian Model Averaging
- Topic 2 – Introduction to nonparametric estimation
- Topic 3 – Stochastic dynamic modelling