About this role
Science question
This project addresses the GW4+ Science for Environmental Solutions priority: an autonomous, satellite-linked payload transforming biodiversity and physical ocean-state monitoring. The proposed use case: anthropogenic noise and ship strike assessment - leading causes of cetacean behavioural change and mortality. Current "risk" maps use historic averages, not real-time whale locations. Scientifically, this project tests whether concurrent acoustic and sea-state sensing can quantify, not just reduce, the uncertainty separating biological signal from noise, establishing a rigorous method underpinning whale vessel-strike mitigation and ocean-state science. Use cases are broad, hence providing agency to the student to direct further development, including ocean-atmosphere fluxes, marine surveillance and species tracking.
Methods
The researcher will develop a payload, building on existing open-source technology, combining a proven Iridium-linked hydrophone system with in-situ wave and sea-state sensing, testing whether onboard sea-state inference reduces whale false detections and improves classification confidence. Machine learning spans acoustic classification (e.g. YOHO) with PML's David Moffat, and sea-state inference with Exeter's Ian Ashton (e.g. MaLCOM), both run at the edge, minimising data transfer. Engineered, bench-tested and deployed in the Celtic Sea to support Exeter’s soundscape research (Matthew Witt), will, generate real-time evidence for threat assessment and marine spatial planning.
Training
The student will be trained in marine acoustics, machine learning and ocean technology, moving across workshop, computer-lab and sea. The candidate will develop hardware and shape acoustic and oceanographic analytical methods alongside the Environmental Intelligence group at PML, and Ashton's research groups, joining Witt's Celtic Sea fieldwork. The student is embedded in supportive, inclusive research environments spanning Exeter-PML.
Partner collaboration: The PML partnership is central: Moffat's acoustic machine-learning expertise, alongside PML's wider mission translating oceanographic and remote-sensing science into environmental solutions, covers ground unavailable at Exeter alone. This pairs Ashton's physical oceanography with Witt's marine ecology, keeping both strands are equally supported.