Statistics and Machine Learning (EPSRC Centre for Doctoral Training) DPhil

<strong>The information provided on this page was correct at the time of publication (November 2025). 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.</strong> <br/><br/>The Statistics and Machine Learning (StatML) Centre for Doctoral Training (CDT) is a four-year DPhil research course (or up to eight years if studying part-time) that will train the next generation of researchers in statistics and machine learning.<br/><br/>This University of Oxford co-hosts the StatML CDT with Imperial College London. This page describes the Oxford component of the course. <br/><br/>The StatML CDT aims to train students to develop widely-applicable novel methodology and theory and create application-specific methods that will lead to breakthroughs in real-world problems in government, medicine, industry and science.<br/><br/>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.<br/><br/>Course structure<br/>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.<br/><br/>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 might lead to your main research project. Alongside your research projects you will engage with taught courses each lasting for two weeks. Core topics will be taught in the first six months of your first year (specific timings may vary for part-time students).<br/><br/>You will then begin your main DPhil project around nine months in to the course (later for part-time students), which can be based on one of the two mini-projects. It is also possible to devise your own project with the help of a supervisor. You will undertake a significant, challenging and original research project, leading to the award of a DPhil. <br/><br/>If you are studying full-time, starting in the second year, you will teach approximately twelve contact hours per year in undergraduate and graduate courses in your host department. If you are studying part-time, teaching will begin in the third year and you will teach approximately six hours per year. This is mentored teaching, beginning with simple marking, to reach a point where individual students are leading whole classes of ten or twelve undergraduate students. Students will have the support of a mentor and get written feedback at the end of each block of teaching.<br/><br/>Throughout the course, you will be required to take other optional courses that usually last two weeks and are delivered in a similar format to the core modules.<br/><br/>Many events bring StatML students and staff together across different peer groups and research groups, ranging from full cohort days and group research skills sessions to summer schools. These events support research and involve staff and students from both Oxford and Imperial coming together at both locations.<br/><br/>The Department of Statistics runs a seminar series in statistics and probability, and a graduate lecture series involving snapshots of the research interests of the department. Several journal-clubs run each term, reading and discussing new research papers as they emerge. These events bring research students together with academic and other research staff in the department to hear about on-going research, and provide an opportunity for networking and socialising.<br/><br/><strong>For the full description, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas</strong>