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Click here to search FindAPhD.com for PhD studentship opportunitiesAbout the Project
Gaining insight from these complex networks demands advanced mathematical and computational techniques for large-scale data processing and analysis. Most work in network science has considered a single type of node linked by a single type of relationship. Yet the actor-relationship networks created from unstructured text can be both multipartite (having many types of node) and multiplex (having many types of relation). Methods for analysing these kinds of network are at the forefront of progress in network science.
This PhD project will develop new methods for identifying key actors in multipartite and multiplex networks derived from unstructured text documents. The student will have the opportunity to work on all aspects of the problem, from use of natural language processing methods to extract entities and relations from large collections of unstructured text documents, to network construction based on these entities and relationships, to application of network statistics and machine learning to identify influential actors. Various datasets and networks are available to underpin this research, as well as a range of tools and software libraries.
This fully funded project is co-sponsored by a fast-growing commercial data science startup with offices in London and Bristol. The student will be based in the computer science department at the Streatham (Exeter) campus of the University of Exeter. They will interact with the vibrant data science research community at Exeter, working with colleagues in mathematics, computer science and relevant quantitative social science disciplines, as appropriate. They will join the “Networks, Data and Complex Systems” research group led by Dr Hywel Williams and receive additional supervision from the commercial sponsor, with opportunities to gain industrial training and experience with real-world big data problems.
Candidates should have a strong background in a quantitative discipline, with programming skills and experience of data analysis. They will learn a variety of techniques in network analysis, machine learning and natural language processing, with excellent opportunities for research publications and further employment in both academic and industrial settings.
Interested candidates are encouraged to contact Dr Hywel Williams ([Email Address Removed]) for further information.