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PhD in Advanced Quantitative Methods at the University of Bristol University of Bristol in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
The pathway is part of the ESRC South West Doctoral Training Partnership and offers funded PhD studentships. Funding may be available through 1+3 and +3 routes. The page also notes that the AQM pathway's master's option is the MSc Geographic Data Science and Spatial Analytics. Self-funded study is possible, with fees listed as £5,343 per year for UK full-time, £2,672 per year for UK part-time, and £22,500 per year for overseas full-time.
Country
United Kingdom
University
University of Bristol
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About this position
The University of Bristol is advertising a PhD in Advanced Quantitative Methods (AQM) within the social sciences. This pathway is designed for research that pushes the boundaries of quantitative methodology, including statistical methods, mathematical modelling, econometrics, computational modelling, machine learning, and artificial intelligence. Projects may develop new quantitative techniques or apply advanced methods to social science data and problems.
The programme is based at the Clifton campus and is delivered on-campus. It is a PhD opportunity with a duration of up to four years full-time or up to eight years part-time. The pathway is part of the ESRC South West Doctoral Training Partnership (SWDTP), which offers funded PhD studentships via the 1+3 and +3 routes. Applicants with a suitable master's degree may enter directly onto the three-year +3 route, while applicants without a suitable master's can apply for the 1+3 route, which includes an ESRC-accredited master's before the PhD.
Entry requirements include at least a 2:1 in a relevant subject, or a master's qualification, or being currently working towards a master's qualification, or an international equivalent in a relevant field. Applicants are asked to contact prospective supervisors before submitting an application. The listed supervisors are William Browne, Richard Harris, George Leckie, and Kate Tilling, all at the University of Bristol.
Funding information on the page notes that the pathway offers funded studentships through ESRC SWDTP, but also that the September 2026 start is for self-funded only. The page lists tuition fees for home and overseas students and notes that funding application deadlines fall well in advance of programme start dates. The application deadline itself is not specified; applicants are directed to contact the course administrator for details.
To apply, use the University of Bristol online application system and select the relevant start date. The page provides a contact email for postgraduate research admissions: [email protected].
Funding details
The pathway is part of the ESRC South West Doctoral Training Partnership and offers funded PhD studentships. Funding may be available through 1+3 and +3 routes. The page also notes that the AQM pathway's master's option is the MSc Geographic Data Science and Spatial Analytics. Self-funded study is possible, with fees listed as £5,343 per year for UK full-time, £2,672 per year for UK part-time, and £22,500 per year for overseas full-time.
What's required
Applicants need at least a 2:1 in a relevant subject, or a master's qualification, or be working towards a master's qualification, or an international equivalent in a relevant field. Applicants must contact prospective supervisors before submitting an application. For ESRC SWDTP funding, students may need to meet additional training requirements and the 1+3 route is for applicants without a suitable master's; the +3 route is for applicants whose master's fulfils ESRC training requirements.
How to apply
Apply through the University of Bristol online application system. Contact prospective supervisors before submitting your application and check the programme admissions statement for supporting documents and entry requirements. For funding applications, contact the course administrator early because deadlines fall well before the programme start dates.
More information can be found here
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