PhD Studentship: Machine Learning for Graphs

Aston University - School of Engineering and Applied Science

(4 years)

Applications are invited to apply for a four year Postgraduate Research studentship, supported by the School of Engineering and Applied Science, to be undertaken within the Computer Science subject group at Aston University. The successful applicant will join the recently established System Analytics Research Institute and the Machine Learning group under the supervision of Dr. Luca Rossi.

This studentship is combined with a teaching assistant role. The successful candidate will be required to provide up to an average of 6 hours per week of teaching support for a distance learning programme; therefore the student must be capable of teaching on an undergraduate course in Software Engineering. Details of teaching responsibilities and a list of taught modules can be found here.

The position is available to start in July 2018 (or earlier by agreement)

Financial Support

This studentship includes a fee bursary to cover the Home/EU tuition fee rate plus a maintenance allowance of £15,500 in 2017/18.

Applicants from outside the EU may apply for this studentship but will need to pay the difference between the ‘Home/EU’ and the ‘Overseas’ tuition fees, currently this is £12,005 for the 2017/18 academic year. As part of the application you will be required to confirm that you have applied for, or, secured this additional funding.

Background of the Project

Graph-based representations have long been used as a powerful way to characterize a large number of systems that are best described in terms of their structure. However, the rich expressiveness of graphs usually comes at the cost of an increased difficulty in applying standard pattern recognition and machine learning techniques to them.

The aim of this project is to investigate novel ways to probe and characterise the structure of graphs. For example, in recent years, classical and quantum walks have emerged as a powerful way to measure the structural similarity between graphs and to define novel vertex signatures. However other processes, such as quantum walks with decoherence, have received little or no attention, despite having a number of unique and interesting properties.

Other open problems that could be considered in this project (depending on the skills of the candidate) are the ability to cope with increasingly large and diverse input graphs (e.g., time-varying and multi-layer graphs), as well as investigating the privacy concerns emerging in the analysis of real-world networks.

Person Specification

The successful applicant will have a strong undergraduate (first class or upper second class) and/or Master’s degree in computer science, engineering, mathematics, physics or a related discipline as well as excellent programming and analytical/mathematical skills. Applicants with expertise and research interests lying at the intersection of graph theory, machine learning, pattern recognition, and physics are particularly welcome to apply. Applicants from non-English speaking countries will need to satisfy Aston’s English language entry requirements.

Enquiries about this project contact Dr Luca Rossi by email:

Details of the online application process can be accessed here.

Applications should be accompanied by a brief review research proposal, and an explanation of how your knowledge and experience will benefit the project.

Applicants are encouraged to contact Dr Luca Rossi in advance to discuss potential research proposals.

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Midlands of England