About KMi

Part-time +/or Distance Learning PhDs at KMi


Although the OU is famous for enabling students to undertake part-time distance learning, doing a PhD is very different to studying a predefined course. A PhD is an apprenticeship in learning a host of new skills 'on the job', and for this reason, most PhD students at the OU are full-time, on-site. In KMi you will get much more from the experience (and have a higher chance of success) if you working as part of a research group, and participating actively in the lab's life (e.g. seeing your supervisor regularly, joining the student group, meeting visiting researchers, giving presentations, and with ready access to high quality technology and internet access).

That being said, there are still part-time students at the OU because full-time study based here is of course not possible for everyone. Moreover, as collaboration technologies improve, some of the time/space obstacles to asynchronous research collaboration are reduced. However, the bottom line is: no supervisor, no PhD! If you can find a supervisor who is happy to take you on a part-time and/or distance learning basis, you're in business.

As explained on the main PhD/Studentship page, all students must write a research proposal, and you are strongly recommended to write this in conjunction with the potential supervisor. What you are recommended to do is look at the list of projects who are looking for students, and approach direct the relevant project leader of any that interest you. Briefly outline your background and interests, and see if they are interested to know more. You can of course send us a proposal out of the blue, but it stands less chance of finding a supervisor to own it.

The application deadline for Part-time study is the same as for Full-time applications.
 
The Open University
 

Multimedia and Information Systems is...


Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.

Visit the MMIS website