Applications are invited for a fully-funded PhD Studentship starting in September 2021 to undertake research in the area of machine learning on graphs.
Graphs are commonly used as a natural representation for data that is best described in terms of its structure, such as chemical compounds, social interactions, and transportation networks. However, graph data brings together with its higher representational power a number of challenges that are not experienced when handling vectorial data. In machine learning, such challenges were tackled with the introduction of graph kernels and more recently graph neural networks. Several open problems exist in both areas, from the need of graph kernel ensembles that can exploit the wealth of kernels developed in these years, to the augmentation of graph data in the context of neural networks. The successful applicant will work in this exciting area to develop new techniques that will advance our ability to operate on graph data.
Applicants should have, or be expected to obtain by the start date, a 1st class or 2:1 degree (or equivalent) in Computer Science or a related subject.
The student will be supervised by Dr Luca Rossi and will be based in the School of Electronic Engineering and Computer Science at the University’s main Mile End campus in East London. Depending on the exact research direction, the student will be participate in either the Networks, Game AI, or Computer Vision group. Queen Mary is a leading research-intensive Russell Group university, ranked 5th among multi-faculty institutions in the UK for research outputs (Research Excellence Framework 2014), and 110th in the world overall (Times Higher Education World University Rankings 2020).
Informal enquiries regarding the post may be made by email to Dr Luca Rossi ([Email Address Removed]).
Applications should be made by following the online process at https://www.qmul.ac.uk/postgraduate/research/subjects/electronic-engineering.html. (“PhD Full-time Electronic Engineering – Semester 1 (September Start)”).
The closing date for applications is Monday 12th April 2021.
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