Training and support
The CDT aims to attract 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., the annual CDT Festival, the bi-monthly Language Lunch, the weekly NLP speaker series, regular industry days).
Cohort-based doctoral training differs from a standard PhD in that you will take part in cohort-wide training modules as part of a more structured programme, rather than training in specific research-based skills as an individual or as part of a small research group on a traditional PhD. CDT students are trained in cohorts of varying sizes.
The UKRI CDT in Natural Language Processing training programme has several advantages over a traditional PhD programme. The collaborative nature of a CDT means there will also be emphasis on multi-disciplinary or inter-disciplinary knowledge, training and research tailored to address the skills needed at doctoral level.
As a CDT NLP trained student, you will receive high-quality training in practical skills as well as acquiring academic knowledge and confidence for your future career - whether in academia or industry.
You will enjoy a supportive environment with plenty of opportunities for collaboration with both academic and non-academic partners to provide you with a diversity of expertise. The CDT NLP also encourages you to make links with industry to develop real-world relevant skills.
As a student on our CDT, you will be expected to participate and contribute fully as a member of your cohort in order to enhance the shared training and development of all peers on the programme.
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.
Equality, Diversity and Inclusivity
We value diversity and inclusiveness and believe that maximising the contribution of every individual enables us all.
Whilst welcoming and supporting freedom of thought and expression, we also seek to embed a culture where all students and staff are treated with respect and feel safe and fulfilled within our community.
Mirella Lapata, Director of CDT in NLP
Women in Computing
School of Informatics’ Athena SWAN Award
School of Informatics’ Equality and Diversity