Vacancies
Academic postions
No current opportunities
Postdoctoral postions
Current opportunites can be found here
Professional, Administrative and Support opportunities
No current opportunities
Funded Ph.D opportunities
Sampling strategies for environmental monitoring networks (PhD)
Supervisors: Claire Miller, Craig Alexander, Craig Wilkie
Relevant research groups: Modelling in Space and Time, Environmental, Ecological, and Sustainability
Funding: This project has specific funding available. More information can be found at the FindAPhD Advert.
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.
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.
Statistical methodology for Assessing the impacts of offshore renewable developments on marine wildlife (PhD)
Supervisors: Janine Illian
Relevant research groups: Modelling in Space and Time, Bayesian Modelling and Inference, Computational Statistics, Environmental, Ecological Sciences and Sustainability
(jointly supervised by Esther Jones and Adam Butler, BIOSS)
Assessing the impacts of offshore renewable developments on marine wildlife is a critical component of the consenting process. A NERC-funded project, ECOWINGS, will provide a step-change in analysing predator-prey dynamics in the marine environment, collecting data across trophic levels against a backdrop of developing wind farms and climate change. Aerial survey and GPS data from multiple species of seabirds will be collected contemporaneously alongside prey data available over the whole water column from an automated surface vehicle and underwater drone.
These methods of data collection will generate 3D space and time profiles of predators and prey, creating a rich source of information and enormous potential for modelling and interrogation. The data present a unique opportunity for experimental design across a dynamic and changing marine ecosystem, which is heavily influenced by local and global anthropogenic activities. However, these data have complex intrinsic spatio-temporal properties, which are challenging to analyse. Significant statistical methods development could be achieved using this system as a case study, contributing to the scientific knowledge base not only in offshore renewables but more generally in the many circumstances where patchy ecological spatio-temporal data are available.
This PhD project will develop spatio-temporal modelling methodology that will allow user to anaylse these exciting - and complex - data sets and help inform our knowledge on the impact of off-shore renewable on wildlife.