Course overview
The PhD/MPhil course is Statistics is delivered by the CDT in Statistics. Unlike many CDTs, this is not dedicated to a single topic, but covers the range of research areas within probability and statistics, to include theoretical and applied probability, mathematical finance, statistical machine learning, computational, methodological, and applied statistics. Our vision is to create a thorough training environment that allows you to develop both depth and breadth of knowledge and skills and fosters innovative and interdisciplinary thinking. We have a dedicated training programme in the first year, designed to optimally prepare you for your PhD project work and opportunities for continuing development through the years.
For the CDT in Statistics, first-year training is split into two parts: a common core, aiming to provide you with an overview of research topics and activity in all areas represented within the department as well as to develop fundamental research skills and an optional core, allowing you to choose from of a wide range of advanced graduate modules, designed for PhD students. This optimally prepares you for the specialised work of the PhD project under the supervision of individual faculty members. You will choose your supervisor and research topic during the first year of the programme. PhD training that is not via the CDT and where the student works with a nominated supervisor from day one is available only in cases where this training scheme is required by the student’s funding source.
Further details of research interests of faculty members and potential PhD projects can be found on individual staff web pages.
Applicants to the CDT do not need a detailed research proposal. However, it will help to indicate your areas of interest in your personal statement, noting that there is no commitment to stay within that area. Applicants who apply for funding from one of the University's scholarship schemes (the Chancellor's International Scholarship or the China Scholarship Council Award) must develop a research proposal in close collaboration with a potential supervisor and are strongly encouraged to contact the department well before applying.
Teaching and learning
The Centre for Doctoral Training (CDT) in Mathematics and Statistics is a four-year program that includes a taught component in the first year with modules aimed specifically at research students. These modules are assessed by coursework and oral examination.
Term one modules, `Milestones in Probability and Statistics’ and `Statistical Frontiers’, are core for all students in the Statistics CDT. They are designed to give you an overview of all research areas and activity in the department, while developing skills such as collaborative work and academic writing and presentation. They aim to give you breadth of knowledge and awareness of research questions and methodologies across all areas of Probability and Statistics.
In term two, you will choose a minimum of two modules from `Graduate topics in Probability’, `Graduate topics in Statistics’, `Graduate topics in Computational Stochastics and Machine Learning’ and `Graduate topics in Applied Probability and Mathematical Finance’. Each topics module consists of three 10h lecture blocks presenting an advanced graduate topic within the corresponding area, taught by an expert. Topics can vary from year to year. By term 2, you will be expected to have some idea of the research area you want work in and term 2 modules are designed to give you in-depth preparation in that area. It is also possible to take additional training outside the Statistics Department (e.g. Mathematics Department, Doctoral College) or the University (e.g. Academy for PhD Training in Statistics or appropriate Summer Schools), as required.
An important part of the research training is the first-year project, which will be conducted from the start of term three until the end of August. You can choose your project from a long list of proposals by potential project supervisors in a wide range of topics. You are also encouraged to discuss and co-create projects with potential supervisors in their research area. Often this project will be the prelude to the actual PhD research.
