PhD Studentship (NERC GW4+ DTP) - AI for Ocean Health: Machine Learning Emulators for Predicting Key Ocean Health Indicators under Climate Change (September 2025 Entry)

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/

Project details

For information relating to the research project please contact the lead Supervisor via c.luo@exeter.ac.uk

Project Aims and Methods

Climate change significantly affects the health of our oceans. Directly influenced ocean health indicators (OHIs) include net primary productivity, oxygen, and pH/acidity, with major impacts on food security, livelihoods and economy, as well as providing crucial feedback to climate. The most direct way to understand how these OHIs respond to the increase in carbon dioxide in the atmosphere and the warming planet is by running multi-decadal simulations (both future projections and hindcasts) of Earth System Models. However, such models are too complex and computationally prohibitive to explore large and complex landscapes of emission scenarios for ‘what-if’ analyses to inform policy and decision-making.  
 
This project will test the hypothesis that machine learning (ML) models can be used to accurately reproduce the results of sophisticated mechanistic marine ecosystem models at a fraction of the computational cost. The central hypothesis is that ML models trained on the outputs of CMIP6 Earth system models will be able to reveal crucial patterns in the responses of OHIs to changes in atmospheric carbon dioxide concentrations, temperature, and other key physical variables. This project will provide valuable tools for researchers, policy makers and environmental managers, particularly benefiting users from developing countries who lack access to high-performance computing. 

Training

The DTP offers funding to undertake specialist training relating to the student’s specialist area of research. 

To apply, please click on the ‘Apply’ button above.

Advert information