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Network modelling of spatial distributions from large data sets

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing either in September 2019 for students seeking funding, or at any point in the academic year for self-funded students. The deadline for funded applications was 31 January 2019.

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 posses 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 .

Funding Notes

This project can be undertaken as a self-funded project, either through your own funds or through a body external to Queen Mary University of London. Self-funded applications are accepted year-round.

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. Further information is available here. We strongly encourage applications from women as they are underrepresented within the School.

We particularly welcome applicants through the China Scholarship Council Scheme.

References

V. Latora, V. Nicosia, G. Russo, “Complex Networks: Principles, Methods and Applications”, Cambridge University Press, 2017.

M. Barthelemy, ``Spatial networks'', Phys. Rep. 499, 1-101 (2011)

S. Boccaletti et al. ``The structure and dynamics of multilayer networks'', Phys. Rep. 544 (1), 1-122 (2014).

How good is research at Queen Mary University of London in Mathematical Sciences?

FTE Category A staff submitted: 34.80

Research output data provided by the Research Excellence Framework (REF)

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