Application Deadline: 28 February 2025
Details
Start date: 01 October 2025
Background
Over the last fifteen years, Statistics & Data Analytics at The University of Glasgow have been working with public sector organisations and industry on the investigation of water quality monitoring networks, network design and statistical modelling of groundwater contamination. Some of the methods developed form the inference engine of a software tool called GWSDAT. More information about GWSDAT is available at https://gwsdat.net as well as its CRAN and Github page.
Project details
In recent years, there has been a lot of work done on investigating how to monitor environmental variables in the most efficient way. Environmental variables, such as pollutants in water, can be monitored through, for example, in-situ sampling, automatic in-situ sensors or remote sensing. However, each sampling approach has different levels of accuracy and is available at different spatial and temporal resolutions.
Environmental regulators and industry all have a responsibility and commitment to monitoring environmental standards and mitigating the potential for increases to levels of pollutants. At a time of world-wide budgetary pressures, the most efficient monitoring schemes are required. However, the mechanisms of monitoring can also be detrimental to the environment e.g. through more visits to a site or lab/computer processing creating a higher environmental footprint.
The aim of the PhD is to extend work already carried out on the optimal design of monitoring networks for spatiotemporal models. Specifically, to identify the spatiotemporal sampling designs that can balance budgetary requirements and environmental impact, with a view to developing and enhancing online tools (e.g. GWSDAT) to provide automatic guidance to practitioners. The latter can then integrate this guidance into their assessment and development of the most optimal monitoring network. This will require statistical methodological development, working on computationally efficient implementations and software development.
Research training and student experience
The successful candidate will be based in the School of Mathematics and Statistics at the University of Glasgow. The school has a vibrant and diverse community of postgraduate research students (100 students, of which 35-40 in Statistics). Postgraduate students have access to a dedicated large communal area and each have their own computer and a shared working space with other postgraduate students. In their first year, the successful candidate will typically take courses offered by the Academy for PhD Training in Statistics (APTS) and will undertake the generic skills and employability training offered by the University. The PhD will be jointly supervised by partners from industry and hence the successful candidate will additionally engage in knowledge exchange/transfer, training and networking in this sector.
How to Apply: Please refer to the following website for details on how to apply:
http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/.
Funding Notes
The studentship will cover both tuition fees (at the UK home level) and stipend for 4 years in line with the Research Council Doctoral stipend level for 2025/26. An international fee waiver might be possible for exceptional candidates.
Eligibility: Applicants must have, or expect to obtain, a UK first-class or upper-second class honours (2.1) degree or equivalent in a relevant discipline.
The successful candidate for this post will demonstrate skills and experience in programming, data analytics and statistical modelling, and a strong interest in environmental research.
