About the Project
A fully funded PhD opportunity to participate in the world-leading research undertaken by the EPSRC Doctoral Landscape Award at the University of Sheffield.
Supervised by Dr Jill Johnson and Prof Paul Blackwell.
The University of Sheffield is one of the leading Russell Group universities in the UK. We carry out cutting-edge research with strong links to industry. When you enrol to do a PhD with us, you will be working with world-leading academics and have access to top of the range facilities. As a PhD student you will have the opportunity to gain skills not only to conduct research, but also to take your career to the next level, whether you want to stay in academia, go into industry or the public sector, or set up your own company. You will have access to a range of training and support services to help you excel in your studies and beyond.
Project Description
In many areas of science, there are multiple useful ways to model a given complex system, which may include simple descriptive models or more elaborate mechanistic or process-based models. A collection of different models, used in combination to give predictions within a formal mathematical and statistical framework, is known as a multi-model ensemble.
Recent research at the University of Sheffield has led to flexible methodology for creating and fitting a multi-model ensemble that allows for the different strengths and weaknesses of models for different types of prediction, rather than simply averaging or weighting the models. It involves constructing a statistical model of the discrepancies between models and between each model and the true, unknown underlying process. The methodology accommodates models with widely differing scope or resolution, or different representations of the system. For example, in ecology, it has been applied to combine models defined at species level with those in which species are merged according to their function in the ecosystem, and to combine multi-species models with one or more single-species models.
The aim of this project is to develop, implement and explore mathematical and statistical extensions to this methodology. In particular, there is a widespread scientific need for a statistical framework that can incorporate spatially explicit models within an ensemble, for example in regional climate models and species distribution models. At present, a spatially explicit model can be included only by aggregating over space within the model, losing information in the process. We aim to develop a framework allowing the spatial structure to be used in the ensemble itself, which would make better use of available information and allow spatially-resolved comparisons and predictions. We will apply these methods to questions in marine ecology and potentially other environmental applications.
The current ensemble approach represents the state of the art for predicting marine ecosystems, for both scientific and management purposes. It is widely used by the UK Centre for Environment, Fisheries and Aquaculture Science (Cefas) which is responsible for official advice on fisheries to the UK government. Cefas will be a formal partner in this PhD project; the supervisor there was involved in the original methodological research. The extensions will address specific environmental scenarios and management options of direct interest to Cefas, leading to better understanding and management of marine ecosystems and fisheries. The project is a unique opportunity to develop novel statistical methodology, with a wide range of potential uses as well as motivation from an important practical application, and with input both on ecology and on modelling from scientists responsible for official advice on fisheries to the UK government.
How to apply
Interested candidates are strongly encouraged to contact the project supervisors to discuss your interest in and suitability for the project prior to submitting your application.
Please refer to the EPSRC DLA webpage for detailed information about the EPSRC DLA and how to apply.
Applicants must ensure that they use the correct application form linked to the EPSRC DLA webpage and must submit an application for October 2027 entry. The form is unique to the EPSRC DLA. It contains the list of advertised projects from which to select, and three questions specific to the DLA.
The application deadline is 17:00 GMT 13 January 2027.
The majority of our DLA studentships will be allocated to candidates who qualify for Home fee status, in accordance with EPSRC funding regulations which place strict limits on international recruitment.
Funding Notes
The award will fund the full tuition fee and a maintenance stipend at the UKRI rate (currently £21,805 per annum) for 3.5 years, as well as a research grant to support costs associated with the project.