Full time funded PhD Studentship
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Stipend: £14,777 pa plus fee bursary, Ref: 11075
Based in Milton Keynes
The Open University’s Knowledge Media Institute (KMi) is a distinct research unit within the Faculty of STEM, and is home to internationally recognized researchers in semantic technologies, data science, educational multimedia, collaboration technologies, artificial intelligence, cognitive science, and human-computer interaction. KMi offers students an intellectually challenging environment with exceptional research and computer facilities, located at the OU headquarters in Milton Keynes, which is 45 minutes by train from central London. Past KMi PhD students pursue successful careers in academia and industry, and have won a number of distinguished awards, including the 2016 and 2017 SWSA Distinguished Dissertation Awards.
We are currently offering a fully-funded studentship commencing October 2018. Applications are invited from UK, EU and ROW for a full time, 3-year study on the following PhD topic. We strongly recommend that you contact Dr Petr Knoth to discuss your interest prior to writing your proposal.
Your proposal is to be based on the following topic:
Large-scale information extraction from unstructured textual resources
(1 position available – 1 position available – supported by JDS:Core).
The student is expected to work with millions of research papers extracting useful information, such as names of scientific methods, statistical tests performed, tables, graphs plus captions, basic metadata (title, affiliation, abstract, author names), conclusion sentences, innovation sentences, methodology sentences, algorithm descriptions, etc., from their text to assist in knowledge discovery and information retrieval. It is expected the student will focus on developing new information extraction methods making use of supervised and semi-supervised machine learning.
The PhD is commercially funded and we have already a reasonably good idea about the direction of the research that can be done. The proposal should therefore demonstrate your capability to learn quickly about the domain as well as to align it with the needs of the PhD project.
Tags: natural language processing, information extraction, machine learning, big data, social media.
- Good knowledge of NLP, text-mining, information extraction or information retrieval;
- Understanding of machine learning fundamentals;
- Excellent programming and especially prototyping skills;
- Ability to process large datasets;
- Understanding of the research lifecycle (ability to state hypotheses, design new methods, implement prototypes, evaluate methods, interpret results, adapt methods, etc.);
- Prior experience of data analysis and machine learning.
You will have a first or upper second class degree from a UK university or the overseas’ equivalent and ideally a relevant Master’s degree. Unless from a majority English-speaking country, non-EEA applicants will require an IELTS score of 6.5 with a minimum of 6 in each element of Listening, Reading, Speaking and Writing. IELTS Certificates are valid for a period of 2-years. All applications are assessed as to their quality, the fit with The Open University research priorities and the availability of supervisors in the relevant field.
Closing date: 17:00 on 12 April 2018
Interviews: to be advised
Equal Opportunity is University Policy
How to apply
It is strongly recommended that applicants contact the named contact point for the project of interest to get more information about the project in question. It is also important to read the online prospectus before downloading and completing the 10-page MPhil/PhD application form.
- Application form if you are resident in the UK or European Economic Area.
- Application form if you are resident elsewhere.
Applications should be sent by email to firstname.lastname@example.org (please CC email@example.com when submitting applications) and should include a covering letter, MPhil/PhD application form, a research proposal (a maximum of 2,000 words), and a full CV, giving contact details for two academic referees.