Location: | Birmingham, Hybrid |
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Salary: | £34,980 to £44,263 |
Hours: | Full Time |
Contract Type: | Fixed-Term/Contract |
Placed On: | 5th March 2024 |
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Closes: | 4th April 2024 |
Job Ref: | 103436 |
Fixed Term contract up to May 2026
Background
The School of Mathematics welcomes applications for a research fellowship in statistics to work on the recently funded £1.5 million UKRI Future Leaders Fellowship project ‘Computational Statistics to Tackle Modern Slavery’, led by Dr Rowland Seymour. The goal of the project is to develop computational statistical methods to map modern slavery. We will then use these methods with partners in the law enforcement and third sectors to understand how modern slavery can be tackled. A video describing the project can be viewed here: https://www.youtube.com/watch?v=IzPuuBnrIDc
This project will develop Bayesian methods, Bayesian nonparametric ones in particular, to better estimate the prevalence of modern slavery. Effective Bayesian nonparametric methods are powerful in problem-based research. Bayesian nonparametric methods replace specific modelling assumptions with weak distributional ones. This provides more accurate inferential and counterfactual analysis, as the methods are not driven by arbitrary modelling assumptions. Analysis using current Bayesian nonparametric methods can be i. slow to carry out, and ii. have larger uncertainty than parametric methods. These issues make Bayesian nonparametric methods unfeasible for researchers in the social sciences and reduce their utility for policy makers. The work in the project will develop efficient computational algorithms to i. perform inference for models with Bayesian nonparametric methods, making them attractive to use, and ii. develop frameworks for dealing with uncertainty in the modelling process. Translating the project findings into meaningful policy decisions will also be an important part of the project. Policy makers at local at national levels will be involved in the project, as well as partners in the law enforcement and third sectors.
Role Summary
Person Specification
Applicants should have a PhD (or near to completion) in Statistics or another relevant subject area, a strong record of research and publications and familiarity with Bayesian Inference and Markov chain Monte Carlo. Please upload your CV, a cover letter (maximum 2 pages), a list of publications and names and emails of three contactable referees.
Informal enquiries to Dr Rowland Seymour (r.g.seymour@bham.ac.uk) or Dr Panayiota Touloupou (p.touloupou@bham.ac.uk).
To download the full job description and details of this position and submit an electronic application online please click on the 'Apply' button.
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We value diversity and inclusion at the University of Birmingham and welcome applications from all sections of the community and are open to discussions around all forms of flexible working
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