PhD opportunity in mathematical and statistical analysis of time series data to quantify trends and events in ocean noise

Posted on Sep 25, 2019 in Job Opportunities in Acoustics

Project Description

In collaboration with the National Physical Laboratory (NPL), the University of Bath is offering a PhD studentship in the field of mathematical and statistical analysis of time series data obtained from deep-ocean noise measurements.

Supervisory Team:

Lead supervisor: Dr Matthew Nunes, Department of Mathematical Sciences, University of Bath, UK
Academic co-supervisors: Dr Philippe Blondel, Department of Physics and Prof Chris Budd, Department of Mathematical Sciences
Industrial co-supervisors at NPL: Dr Peter Harris (Data Science) and Stephen Robinson (Ultrasonics and Underwater Acoustics)

Variation in the ambient sound levels in the deep ocean has been the subject of a number of recent studies, with particular interest in the identification of trends, features and events in the data. Many early studies demonstrated the effect of shipping on low frequency sound dynamics in the deep-ocean. However, it is now recognised that there is a variety of other sound sources, both natural and man-made, contributing to the soundscapes. In addition, climatic variations like warmer oceans or sea ice cover can influence sound propagation over large distances.

This project is focussed on data from the hydro-acoustic monitoring stations of the Preparatory Commission for the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO). Additionally, the investigation will make use of other quantities, such as climate variables, obtained from public databases including those managed by the US National Oceanic and Atmospheric Administration (NOAA) and National Snow and Ice Data Center (NSIDC). The global monitoring stations located within the major ocean basins constitute a spatially distributed network of sensors that provide continuous measured data over long time periods – a very large and rich data set, containing signals from human sources such as shipping and offshore geophysical surveys, as well as natural sources such as baleen whales, geological and seismic events, weather phenomena and ice breaking.

Given the high-dimensional and complex nature of the dataset, the aim of this project is to develop a modelling framework to reveal intersource and intersensor dependencies in the data, i.e. to extract quantitative information about sound levels in the deep ocean from the data. The analysis methodology developed in the project will include aspects of signal processing, statistical characterisation of dynamics and machine learning techniques. Being able to separate and efficiently analyse sources of noise in this complex environment will lead to improved understanding of the local and global causes of fluctuations in ocean acoustics, and have potential impact in short- / long-term environmental planning and marine conservation.

The successful candidate should have a first class or 2:1 degree in Mathematics, Statistics or another relevant discipline. A Masters qualification in Mathematics or Statistics would be beneficial. Some experience with time series modelling or signal processing is desirable.


Informal enquiries should be directed to Dr Matthew Nunes on email address .

Formal applications should be made via the University of Bath’s online application form:

Please ensure that you quote the supervisor’s name and project title in the ‘Your research interests’ section.

More information about applying for a PhD at Bath may be found here:

Anticipated start date: 20 January 2020 or earlier.

Funding Notes

UK and EU candidates applying for this project will be considered for a studentship which will cover UK/EU tuition fees, a training support fee of £1,000 per annum and a tax-free maintenance allowance at the UKRI Doctoral Stipend rate (£15,009 in 2019-20) for a period of up to 3.5 years.

Unfortunately, candidates who are classed as Overseas for fee paying purposes are not eligible for the studentship.

Closing date for applications: 31 October 2019