Data-driven spatial models of human societies
PhDs and postgraduate research
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
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2021/2022 fees (applicable for October 2021 and February 2022 start)
PhD and MPhil
Home/EU/CI full-time students: £4,500 p/a**
Home/EU/CI part-time students: £2,250 p/a**
International full-time students: £17,600 p/a*
International part-time students: £8,800 p/a*
PhD by Publication
External candidates: £4,407*
Members of staff: £1,720
All fees are subject to annual increase. If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
- 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. 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.