Themes

Here are some core text mining themes (please see below for details) that are currently the focus in our TEAM:

• Text analytics and sentiment analysis: identification of subjective opinion and sentiment features from user-generated content (e.g. blog mining, tweets, etc.);
• Extracting negations, contrasts and contradictions: identification of utterances that are negated, or contrast or contradict some other expressions (both explicit and implicit);
• Concept mining and structuring: learning and identification of concepts and terminology from text, including their structuring (internal and external);
• Temporal text analytics: identification of temporal expressions and their scope in text;
• Integrated text and data mining: combining the results from different perspectives using various methods from machine learning;
• Text processing midleware for the Semantic Web: building an infrastructure to support building text mining solutions for the Semantic Web (identification of concepts, links, etc);

and these are preferred application areas:

• Biology and biomedicine (molecular interactions, cancer studies, characterisation of molecular events, etc.)
• Bioinformatics and computational biology (tools, services, resources, methods)
• Clinical medicine and health-care (clinical decision support, quality of life monitoring)
• E-science, e-commerce and e-government (e.g. monitoring, tracking, dissemination of information)
• Engineering (knowledge management)

You would typically ‘select’ a topic that consist of a particular theme in a specific application area. I’d be also happy to consider proposals in the areas of multi-lingual text mining and NLP for Serbian.

Application steps

You will be expected to have passion for text processing, in addition to an excellent first degree in computer science or related area. Some experience in natural language processing is very useful, whereas very good programming experience (in a combination of programming languages) is a must. If you belive you’ve got all these, send an email to Goran Nenadic (see below) with a full CV and a brief note as why you would like to do PhD in our TEAM. Please allow some time for us to reply. Contact email: G.Nenadic@manchester.ac.uk.

Funding

PhD studies are between 3 and 4 years, typically closer to 4 than to 3 years. There is only one route for securing funding: the candidate needs to be outstanding. There are 3 possible sources of funding:

• specific, pre-defined projects (NONE CURRENTLY),
• funding from the School of Computer Science (see here for details) and
• external funding (private, external bodies – e.g. foreign governments, etc).

Environment

The School of Computer Science is one of the leading Schools in the UK reknown for the excellence of its research. The world’s first computer with internal memory was build in the School and Alan Turing has laid the foundations of Computer Science and Artificial intelligence while in Manchester. The international reputation of our research reflects on its high ranking in the last national Research Assessment Exercise (RAE), which places the School among the best five Computer Science departments in the UK and top in England for research power. The School has a vibrant research environment with more than 150 PhD students, 90 research staff and 70 academic staff.

Our research TEAM is part of the Text Mining/NLP research group, which hosts the UK National Centre for Text Mining. We are also affiliated to the Manchester Interdisciplinary BioCentre. The team is vibrant, diverse and very much international.