Research ThemeSensor Signal ProcessingAimThis project aims to significantly reduce the manual labelling of underwater data (images or acoustics) using other available data sources via transfer learning, while understanding the uncertainty of both the learned transfer functions and the uncertainty in the resulting predictions by adapting a Bayesian framework.ObjectivesLiterature review on Bayesian transfer learning (BTL)Develop underwater BTL framework, incorporating known physical effects (such as wave distortion by water); data source can be e.g. imagery or acoustics, depending on the student’s and project partner’s interestAnalyse existing underwater data set and test methodologyDevelop online BTL algorithm that can process data in real time DescriptionModern machine learning methods require large amounts of training data for reliable predictions, and can degrade significantly in performance when the test data differs from the training data distribution. In transfer learning, one has access to a source data set (e.g. vessel sounds in UK coastal waters) as well as a small amount of data from a target data set for which data collection is costly or difficult (e.g. acoustic signature of unknown foreign vessels in remote waters). The goal of this project is to develop an underwater Bayesian transfer learning framework, which allows for rigorous uncertainty quantification of both the transfer function and the resulting predictions – i.e. we can understand both how well we can expect to perform on our target data in general, as well as how certain we are about predictions for specific observations. Closing date: Sat, 31/01/2026 - 12:00 Apply now Principal Supervisor Dr Torben Sell 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. A solid background in statistics or a related quantitative discipline is of benefit. The student will be expected to work in the intersection of statistics, machine learning, and marine science, and should thus be interested in interdisciplinary work.Further information on English language requirements for EU/Overseas applicants. Funding Full funding is available for this position.
