About this project
This PhD project will research and develop innovative natural computation techniques for scalable analysis of big complex network data. This involves tasks such as identification of bottlenecks and decompositions of large networks into sub-networks. A typical example is the identification of “bridges” that partition very large social networks. Such splits allow a social network to be decomposed into smaller networks, which can be analysed using more demanding computational methods that can use high performance computing techniques. Such results will pave the way to a reduction of computational time and resources to solve problems in big complex network data. Applications areas include community identification, creation of large network maps, and robustness assessment and improvement. Research outcomes apply to biological and engineering problems, such as analysis of protein-protein interactions and optimisation of energy and utility distribution networks.
Lead Supervisor: Dr David Chalupad.email@example.com
Prof Ken Hawick
Dr James Walker
Full-time UK/EU PhD Scholarships will include fees at the ‘home/EU' student rate and maintenance (£14,121 in 2016/17) for three years, depending on satisfactory progress.
Full-time International Fee PhD Studentships will include full fees at the International student rate for three years, dependent on satisfactory progress.
Candidates should have excellent programming skills and a degree in Computer Science or a related discipline. Experience in machine learning is essential. Further knowledge and expertise of one or more of the following areas is highly desirable: evolutionary algorithms, deep learning, natural language processing, embedded systems and the Internet of Things (IoT).
Successful applicants will be informed of the award as soon as possible and by 8th May 2017 at the latest.
Find out more about research in Computer Science.