Data-driven spatial models of human societies
Self-funded PhD students only
School of Mathematics and Physics
Applications accepted all year round
The work on this project will:
- make use of techniques from statistical learning to search for features from society’s “output signal” (online news, social media, government health/ social / economic data)
- construct models for the evolution of these measurable features, inspired by classical models of statistical physics
Despite rapid advances in the connectivity of our modern world it remains true that “everybody’s got to be somewhere”. The mathematical significance of this statement is that the networks of human connections – through which innovations, ideas, language, political opinions and money all travel – are embedded in two dimensional space.
It is well known to statistical physicists that the spatial dimension of a system of interacting particles has a profound impact on their collective behaviour. This observation also applies to human social systems, and recent work has shown that analogies of classical two-dimensional models of interacting atoms may be used to predict the spatial evolution of human language.
At the same time, there has been an explosion in the availability of geographically-located social, economic and textual data generated by governments, charities, social media, local newspapers, businesses and online forums. The aim of this project is first to make use of techniques from statistical learning to search for features from this human output which exhibit significant spatial or spatial-temporal variations, and are therefore powerful predictors of location.
The discovery of these features may then been brought together with our understanding of the ‘‘social physics’’ of human societies to construct models for the evolution of these measurable features, inspired by classical models of statistical physics.
 J. Burridge Spatial evolution of human dialects. Phys. Rev. X 7, 031008 – Published 17 July 2017
- You'll need a good first degree from an internationally recognised university (minimum second class or equivalent, depending on your chosen course) or a Master’s degree in a relevant subject area
- In exceptional cases, we may consider equivalent professional experience and/or Qualifications
- English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0
We’d welcome applications from candidates with Knowledge of Probability and Stochastic Processes, Python or other programming language and experience with working with Data.
How to apply
Please contact Dr James Burridge (email@example.com) to discuss your interest before you apply, quoting the project code.
When you're ready to apply, you can use our online application form and select ‘Mathematics and Physics’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Our ‘How to Apply’ page offers further guidance on the PhD application process.
Please note, to be considered for this self-funded PhD opportunity you must quote project code MPHY4460219 when applying.