Network modelling of spatial distributions from large data sets

The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing in September 2021.

This project will be supervised by Dr. Vincenzo Nicosia.

Description: The aim of this project is to devise models and measures for network-based characterisation of spatial information about human activity. In particular, the thesis will be focused on the investigation of new methods to construct network representations of spatial distributions (e.g. through simple, multi-layer, and time-varying graphs), on the quantification of the properties of those distributions by means of appropriate network descriptors, and on the construction of mechanistic models able to reproduce stylised facts of those data sets. Although the project is mainly methodological, there will be the opportunity to test the proposed models and measures on large data sets of real-world spatial systems, including metropolitan environments, census data, online social networks, and brain networks. The prospect candidate will possess a well-balanced mixture of mathematical and computational abilities, and should ideally have a solid background in at least two subject among discrete maths, random processes, time series analysis, graph theory, network science, scientific computing.

The application procedure is described on the School website. For further inquiries please contact Dr. Vincenzo Nicosia at [Email Address Removed]. Funding may be available through School of Mathematical Sciences Studentships, EPSRC DTP, and the S&E BAME Doctoral Research Studentship, in competition with all other PhD applications. Studentships will cover tuition fees, and a stipend at standard rates for 3-3.5 years. Applicants interested in the full funding will have to participate in a highly competitive selection process. The School of Mathematical Sciences is committed to the equality of opportunities and to advancing women’s careers. As holders of a Bronze Athena SWAN award we offer family friendly benefits and support part-time study.