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[交流] 西交利物浦大学计算机和软件工程系博士研究生（全额奖学金，英国利物浦大学博士学位）

Project Description
Data on collaborative social Web sites (e.g., tags), contributed by millions of online users, represents an essential source for the “collective intelligence”, which many researchers have attempted to explore and exploit. The tags and the resulting folksonomies are originally utilised as efficient means for content annotation, organisation and discovery. However, over the years they remain tacitly, gradually developing into a dormant collection of noisy, low-quality and often ambiguous “keywords”, which demonstrate little usefulness for content discovery and search on the information-rich social Web of tremendous scale. To a great extent, the situation can be attributed to the lack of effective methods and techniques for deriving real semantics from the user generated data. Existing knowledge structures manually created by human are accurate and of high quality, and have been used in many applications for search, annotation and classification, however, the major limitation is that they are shallow, extremely costly to maintain, and tend to become obsolescent after certain amount of time. The proposed research applies techniques developed in the areas of semantic Web, machine learning and data mining to extract knowledge from the large-scale collaboratively generated data on the social Web, and attempts to move one step further towards harvesting the “collective intelligence”, in particular, in the domain of scientific research. It aims to develop new learning methods and algorithms to differentiate and unveil the true semantics of tags as well as the relations among them, and to abstract them in formal taxonomical or ontological knowledge. More importantly, the research aims to elicit evolving semantics from the social dynamics and to implement efficient learning mechanisms to address the computational challenges. The significance of the research is to enable better understanding of the social interactions for human by deriving evolving knowledge from collectively generated contents and to improve content discovery, search, ranking and attention support in general.

Supervision:
1st supervisor: Dr. Wei Wang, Xi'an Jiaotong Liverpool University, (email: wei.wang03@xjtlu.edu.cn), http://academic.xjtlu.edu.cn/csse/Staff/wei-wang
2nd supervisor: Prof. Frans Coenen, University of Liverppol, http://www.csc.liv.ac.uk/~frans/
3rd supervisor: Asso. Prof. Kevin Kam Fung Yuen, Xi'an Jiaotong Liverpool University, http://academic.xjtlu.edu.cn/csse/Staff/kevin-yuen

Requirements:
The candidate should have a first class or upper second class honours degree, or a master’s degree (or equivalent qualification), in computer science or information science. Evidence of good spoken and written English is essential. The candidate should have an IELTS score of 6.5 or above, or an equivalent qualification, if the first language is not English. This position is open to all qualified candidates irrespective of nationality.

Preferences will be given to those with experiences in machine learning, data mining and data/knowledge engineering from texts, strong programming skills and capabilities to rapid prototyping.

Degree:
The student will be awarded a PhD degree from the University of Liverpool (UK) upon successful completion of the program.

Funding:
The PhD studentship is available for three years subject to satisfactory progress by the student. The award covers tuition fees for three years (currently equivalent to RMB 80,000 per annum) and provides a monthly stipend of 3500 RMB as a contribution to living expenses. It also provides up to RMB 16,500 to allow participation at international conferences during the period of the award. It is a condition of the award that holders of XJTLU PhD scholarships carry out 300-500 hours of teaching assistance work per year. The scholarship holder is expected to carry out the major part of his or her research at XJTLU in Suzhou, China. However, he or she is eligible for a research study visit to the University of Liverpool of up to three months, if this is required by the project.

For more information about doctoral scholarship and PhD programme at Xi’an Jiaotong-Liverpool University (XJTLU): Please visit
http://www.xjtlu.edu.cn/en/admissions/postgraduate/phd-degree/ feesscholarships.html
http://www.xjtlu.edu.cn/en/admis ... te/phd-degree.html.

How to Apply:
Interested applicants are advised to email the following documents to Doctoralstudies@xjtlu.edu.cn (please put the project title and primary supervisor’s name in the subject line).

CV
Two reference letters
Personal statement outlining your interest in the position
Proof of English language proficiency (an IELTS score of above 6.5 or equivalent is required
Verified school transcripts in both Chinese and English (for international students, only the English version is required)
Verified certificates of education qualifications in both Chinese and English (for international students, only the English version is required)

Contact:
Informal enquiries may be addressed to Dr. Wei Wang (wei.wang03@xjtlu.edu.cn), whose personal profile is linked below, http://academic.xjtlu.edu.cn/csse/Staff/wei-wang

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