A more flexible training approach than a three-year PhD
The CDT attracts students from a diverse range of backgrounds and disciplines, including computer science, AI, maths and statistics, engineering, linguistics, cognitive science, and psychology.
Such an interdisciplinary cohort requires a training approach that is more flexible than the standard three-year PhD, which is why this programme takes the form of a four-year PhD with integrated training. It interleaves training at the level of a master's degree (180 credits of courses and project work) with PhD research (540 credits). The advantages of this structure are:
- By mixing courses and PhD work, students gradually progress from classroom teaching to independent research. At the same time, research will inform their learning experience from the first day, and they can immediately apply skills learned in the classroom to their PhD project.
- Students can take the courses that are relevant to their research when they need them, rather than having to anticipate all their training needs in advance and front-load all their courses in year 1.
- The degree structure allows for maximum flexibility to accommodate a cohort of students with a wide range of backgrounds. Students who have a lot of prior NLP training, for example, would be expected to do a research-heavy first year (followed by advanced courses informed by their PhD project), while students with less relevant backgrounds can take a larger number of foundational courses upfront.
- While all students select an individual set of courses, there are also shared components that everyone takes, which together with a programme of staff and student led events will promote cohort formation (e.g. Annual CDT Festival, Bi-monthly Language Lunch, Weekly NLP speaker series, regular Industry days, etc).