PhD Studentship: Building a digital twin navigating autonomous underwater vehicles to monitor water quality, NERC GW4+ DTP PhD studentship for September 2025 Entry Ref: 5433

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 jos@pml.ac.uk

Project Aims and Methods

Autonomous underwater vehicles (AUVs) are establishing themselves as a key source of marine observations. Excitingly, AUVs can be navigated by “intelligent’’ digital-twin (DT) systems based on two-way communication with physical-biogeochemistry models and machine learning components. This maximises the AUV effectiveness and impact by ensuring they target the most interesting/important locations and times, reducing the cost and carbon footprint. The supervisory team is working at the forefront of DT solutions to AUVs, applying them to tracking of phytoplankton blooms (a study: doi.org/10.3389/fmars.2022.1067174) and in ongoing NERC-funded mission tracking harmful algae blooms (HABs) with resulting oxygen depletion (https://www.pml.ac.uk/News/PML-successfully-deploy-a-fleet-of-ocean-robots-to).  

However, several assumptions used in those DT systems can be challenged, including those behind design of path-planning algorithms, used sampling setup, merging observations across very different spatio-temporal scales, treatment of biases among different data sources, or neglecting correlations in observations and among model variables. We propose for the student to address those assumptions through a synthetic DT approach where the model output represents ``real-world ocean’’ in which virtual AUVs take ``samples’’, with all model and observational errors known. Using this approach the student will simulate virtual multi-glider mission experiments, simultaneously tracking HABs and oxygen depletion, finding the optimal DT design to inform future missions.

Training

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

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