Training and support

The CDT aims to attract students from a diverse range of backgrounds, including computer science, AI, maths and statistics, engineering, linguistics, cognitive science, and psychology. This 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 program of staff- and student-led events will promote cohort formation (e.g., the annual CDT Festival, the bimonthly Language Lunch, the weekly NLP speaker series, regular industry days).


Contemporary research in NLP uses complex machine learning models such as neural networks, and thus requires considerable computing resources. CDT students will have access to a large GPU (graphics processing unit) cluster and to a terabyte storage array, both dedicated for NLP research. Furthermore, they will have access to the Edinburgh Compute and Data Facility (ECDF), a central University resource that maintains a cluster of over 4,000 compute cores and a large high performance storage facility. A number of our industrial partners provide in-kind support to the CDT in the form of compute credits, GPU hardware, and access to proprietary datasets.

Some of the PhD projects conducted under the auspices of the CDT will involve lab-based experiments that investigate human language and speech processing. Students will use our state of the art experimental facility comprising sound studios, an anechoic chamber, an eye-tracking lab with three high resolution trackers, and a suite of experimental booths for perception experiments. The Bayes Centre includes a dedicated virtual/augmented reality lab combined with motion capture and eye-tracking.