Self-funded
MPHY4460219
October, February and April
Applications accepted all year round
This 3-year self-funded PhD will be based in the School of Mathematics and Physics and will be supervised by Dr James Burridge.
- make use of techniques from statistical learning to search for features from society鈥檚 鈥渙utput signal鈥 (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 鈥渆verybody鈥檚 got to be somewhere鈥. The mathematical significance of this statement is that the networks of human connections 鈥 through which innovations, ideas, language, political opinions and money all travel 鈥 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[1] 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 鈥樷榮ocial physics鈥欌 of human societies to construct models for the evolution of these measurable features, inspired by classical models of statistical physics.
References
[1] J. Burridge Spatial evolution of human dialects. Phys. Rev. X 7, 031008 鈥 Published 17 July 2017
Visit the research subject area page for fees and funding information for this project.
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK (UK and EU students only).
Bench fees
Some PhD projects may include additional fees 鈥 known as bench fees 鈥 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.
Entry requirements
- 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鈥檚 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鈥檇 welcome applications from candidates with Knowledge of Probability and Stochastic Processes, Python or other programming language and experience with working with Data.
When you are ready to apply, please follow the 'Apply now' link on the Mathematics PhD subject area page and select the link for the relevant intake. 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 鈥How to Apply鈥 page offers further guidance on the PhD application process.