Course summary

The information provided on this page was correct at the time of publication (November 2024). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas. The Statistics and Machine Learning (StatML) Centre for Doctoral Training (CDT) is a four-year DPhil research course (or eight years if studying part-time). It will train the next generation of researchers in statistics and machine learning, who will develop widely-applicable novel methodology and theory and create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. This is the Oxford component of the StatML CDT, co-hosted by Imperial College London and the University of Oxford. The course will provide you with training in both cutting-edge research methodologies and the development of business and transferable skills – essential elements required by employers in industry and business. You will undertake a significant, challenging and original research project, leading to the award of a DPhil. Given the breadth and depth of the research teams at Imperial College and the University of Oxford, the proposed projects will range from theoretical to computational and applied aspects of statistics and machine learning, with a large number of projects involving strong methodological/theoretical developments together with challenging real-world problems. A significant number of projects will be co-supervised with industry. You will pursue two mini-projects during your first year (specific timings may vary for part-time students), with the expectation that one of them will lead to your main research project. At the admissions stage you will choose a mini-project. These mini-projects are proposed by the department's supervisory pool and industrial partners. You will be based at the home institution of your main supervisor of the first mini-project. If your studentship is funded or co-funded by an external partner, the second mini-project will be with the same external partner but will explore a different question. Alongside your research projects you will engage with taught courses each lasting for two weeks. Core topics will be taught at the beginning of your first year (specific timings may vary for part-time students) and are:

• Modern Statistical Theory

• Statistical Machine Learning;

• Bayesian methods and computation.