Research ThemeSensor Signal ProcessingAimDevelop algorithms for detecting and classifying natural and manmade objects in the ocean environment.ObjectivesUnderstand the background ocean environment as a reference for detecting objects.Characterize the acoustic signals emitted by vessels travelling on the surface of the sea and marine mammals.Develop object detection algorithms and analyze their performance in terms of accuracy, explainability, and robustness to variations in environmental conditions.DescriptionPassive sonar is a key sensing modality for achieving understanding of the ocean environment, to help protect naval platforms. This technology can be applied in a range of systems, from hull-mounted or towed hydrophone arrays to sonobuoys or uncrewed underwater vehicles. Sonar contact classification is a challenge, due to the need to detect, label, and track multiple targets. Much of this work is currently carried out manually by sonar operators. The growing risk from increasingly stealthy targets, complex environments, and a data deluge from more capable sensors with more channels, necessitates new automatic approaches to marine object detection and localization. Recent developments in the fields of artificial intelligence (AI) and machine learning (ML) offer promise for improvements in the analysis of acoustic signals, in terms of speed, accuracy, and robustness. This project will investigate novel approaches to analyzing the data and provide an increased understanding of the maritime arena.In later years of the EngD/PhD, QinetiQ would like to investigate the possibility of an internship for the student for up to 6 months. Further information QinetiQ has developed an algorithm for detecting the time stamps of sound cuts of short mammal calls in the master tapes of the Watkins Marine Mammal Sound Database. This enables the development and assessment of object detection algorithms as opposed to just classification of sound cuts, which is what the database is usually used for. The algorithm is not yet perfect, but could be made available to the student to use and improve on.QinetiQ has access to unclassified recordings of surface vessels. Subject to permission of the data owner, these could be made available to the student to support the research.QinetiQ: https://www.qinetiq.comWatkins: https://whoicf2.whoi.edu/science/B/whalesounds/index.cfm Closing date: Sat, 31/01/2026 - 12:00 Apply now Principal Supervisor Dr Henry Gouk Eligibility Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants. Funding Full funding is available for this position.
