The Research Group in Computational Linguistics invites applications for TWO 3-year PhD studentships in the area of translation technology. These two PhD studentships are part of a larger university investment which includes other PhD students and members of staff with the aim to strengthen the existing research undertaken by members of the group in this area. These funded student bursaries consist of a stipend towards living expenses (£14,500 per year) and remission of fees.
We invite applications in the area of translation technology defined in the broadest sense possible and ranging from advanced methods in machine translation to user studies which involves using of technology in the translation process. We welcome proposals focusing on Natural Language Processing techniques for translation memory systems and translation tools in general. Given the current research interests of the group and its focus on computational approaches, we would be interested in topics including but not limited to:
- Enhancing retrieval and matching from translation memories with linguistic information
- The use of deep learning (and in general, statistical) techniques in translation memories
- (Machine) translation of user generated content
- The use of machine translation in cross-lingual applications (with particular interest in sentiment analysis, automatic summarisation and question answering)
- Phraseology and computational treatment of multi-word expressions in machine translation and translation memory systems
- Quality estimation for translation professionals
Other topics will be also considered as long as they align with the interests of the group. The appointed student is expected to work on a project that has a significant computational component. For this reason we expect that the successful candidate will have good background in computer science and programming.
The application deadline is 5th January 2018 and Skype interviews with the shortlisted candidates will be organised shortly after the deadline. The starting date of the PhD positions is as soon as possible after the offer is made.
A successful applicant must have:
- A good honours degree or equivalent in Computational Linguistics, Computer Science, Translation studies or Linguistics
- A strong programming and statistical / Mathematical background or closely related areas (if relevant to the proposed topic).
- Experience in Computational Linguistics / Natural Language Processing, including statistical, Machine Learning and Deep Learning, applications to Natural Language Processing.
- Experience with translation technology
Experience with programming languages such as Python, Java or R is a plus
An IELTS certificate with a score of 6.5 is required from candidates whose native language is not English. If a certificate is not available at the time of application, the successful candidate must be able to obtain it within one month from the offer being made.
Candidates from both UK/EU and non-EU can apply.
Applications must include:
- A curriculum vitae indicating degrees obtained, courses covered, publications, relevant work experience and names of two referees that could be contacted if necessary
- A research statement which outlines the topics of interest. More information about the expected structure of the research statement can be found at https://www.wlv.ac.uk/media/departments/star-office/documents/Guidelines-for-completion-of-Research-Statement.doc
These documents will have to be sent by email before the deadline to Amanda Bloore (A.Bloore@wlv.ac.uk). Informal enquiries can be sent to Constantin Orasan (C.Orasan@wlv.ac.uk)
Established by Prof Mitkov in 1998, the research group in Computational Linguistics delivers cutting-edge research in a number of NLP areas. The results from the latest Research Evaluation Framework confirm the research group in Computational Linguistics as one of the top performers in UK research with its research defined as ‘internationally leading, internationally excellent and internationally recognised’. The research group has recently completed successfully the coordination of the EXPERT project a successful EC Marie Curie Initial Training Network promoting research, development and use of data-driven technologies in machine translation and translation technology (http://expert-itn.eu)