Dr David Moffat
Artificial Intelligence and Data Lead Scientist
dmof@pml.ac.uk | +44 (0)1752 633100 (switchboard)David Moffat is an AI and Machine Learning Data Scientist and leads on the AI work across the organisation. With a strong foundation in computer science and extensive experience in applying AI and data science techniques across diverse datasets, David is currently focused on advancing environmental and marine science research. His work involves leveraging both modern and traditional AI methodologies to enhance our understanding of the natural world.
David’s expertise lies in applied AI, particularly in signal processing and time series data analysis. He holds a BSc in Artificial Intelligence and Computer Science from the University of Edinburgh and an MSc. in Signal Processing and PhD. in Computer Science from Queen Mary University of London. Following his academic journey, he has held roles as a postdoctoral researcher, university lecturer, and now leads innovative AI-driven initiatives at Plymouth Marine Laboratory. David has authored 16 peer-reviewed articles, 13 peer-reviewed conference papers, and four book chapters. He has also delivered AI and Earth Observation training courses to over 300 participants. In addition to his academic accomplishments, he has a background in technical support, project management, and research leadership.
David has been featured in an IBM blog post about a collaboration between PML, UK Science and Technology Facilities Council (STFC) Hartree Centre, University of Exeter and IBM Research about the release of a first-of-its kind foundation model for the ocean, called Granite-Geospatial-Ocean.
Key Projects
Selected Publications
- Dawson, G., Vandaele, R., Taylor, A., Moffat, D., Tamura-Wicks, H., Jackson, S., Lickorish, R., Fraccaro, P., Williams, H., Luo, C. and Jones, A., 2025. “A Sentinel-3 foundation model for ocean colour”. arXiv preprint arXiv:2509.21273 https://doi.org/10.48550/arXiv.2509.21273
- Lucie A. Delobel, David Moffat, Emma Tebbs, Andreas C.W. Baas, “Segmenting and characterising ripple patterns on sand dunes using machine learning and 2D semi-variogram.” Remote Sensing of Environment Volume 331 (2025): 115031 https://doi.org/10.1016/j.rse.2025.115031
- Lauren Biermann, David Moffat, Clive E. Sabel and Thomas E. Stovin “The theoretical role of the wind in aerosolising microplastics and nanoplastics from coastal combined sewer overflows.” Nature Scientific Reports 15, 23623. July 2025 https://doi.org/10.1038/s41598-025-06115-5
- Ming-Xi Yang, David Moffat, Yuanxu Dong and Jean-Raymond Bidlot “Deciphering the variability in air-sea gas transfer due to sea state and wind history.” PNAS Nexus, page 389. September 2024 https://doi.org/10.1093/pnasnexus/pgae389
- Aser Mata, David Moffat, Sílvia Almeida, Marko Radeta, William Jay, Nigel Mortimer, Katie Awty-Carroll, Oliver R. Thomas, Vanda Brotas, Steve Groom “Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas.” Ecological Informatics. 102708, July 2024 https://doi.org/10.1016/j.ecoinf.2024.102708
- Timothy J. Smyth, David Moffat, Glen A. Tarran, Shubha Sathyendranath, François Ribalet and John Casey “Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale.” Frontiers in Marine Science. Volume 10, July 2023 https://doi.org/10.3389/fmars.2023.1191216
