PhD Position in Machine Learning for the Analysis of Ambient Marine Noise
University of Southampton School of Electronics and Computer Science
United Kingdom

PhD Studentship - Machine learning for the analysis of ambient marine noise

School of Electronics & Computer Science

Location: Highfield Campus

Closing Date: Friday 25 June 2021

Reference: 1367221FP

Supervisory Team: Mohammed Belal, Christine Evers and Tim Norman

Project description

Identifying and distinguishing among events in the marine environment is an essential task in developing better understanding of climate change, and animal and human behaviour across 71% of the planet. Sources of ambient noise in the marine environment can be classified into natural (sediment flows, volcanic geo-hazards, etc.) and anthropogenic (ocean bottom trawling, offshore drilling, etc.). The aim of this research is to radically improve ocean observation and visualization capabilities, both for oceanographic research and for various marine sector applications of national and strategic importance. This studentship is part of a wider research collaboration between NOC and the University of Southampton to combine expertise in densely distributed big-data acquisition (using standard telecommunications fibre-optic cables) and machine learning and AI techniques to characterise and automatically identify patterns in this data to aid human understanding of the environment.

The key challenges in this project stem from the volume of streaming data generated and the lack of substantial quantities of labelled signals. Through the project we will investigate the use of methods such as t-SNE to reduce the dimensionality of high velocity data streams and semi-supervised learning methods to start to address the challenges of localisation, tracking and discrimination of key events in the marine environment.

This project is funded through the UKRI MINDS Centre for Doctoral Training ( This is one of 16 PhD training centres in the UK with a unique focus on advancing AI techniques in the context of real-world engineered systems with a remit that spans novel hardware for AI, AI and machine learning, pervasive systems and IoT, and human-AI collaboration. We provide enhanced cross-disciplinary training in electronics and AI, entrepreneurship, responsible research and innovation, communication strategies, outreach and impact development as part of an integrated 4-year iPhD programme.

The MINDS CDT is based in a dedicated laboratory on Highfield Campus at the University of Southampton. The lab provides a supportive environment for individual research, ideas sharing and collaboration, and the wider campus provides access to substantial high-performance computing (including dedicated GPU servers), maker and cleanroom facilities. You will take part in our annual, student-designed innovation camps, be able to work with industry and government partners through our internship scheme and be able to take part in exchanges with international university partners.

If you wish to discuss any details of the project informally, please contact the MINDS CDT, Email:,

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 25 June 2021 for entry in October 2021.

Funding: full tuition for UK Students an enhanced stipend £18,285, tax-free per annum for 4 years.

How To Apply

Applications should be made online. Select programme type (Research), 2021/22, Faculty of Physical Sciences and Engineering, next page select “iPhD Machine Intelligence for Nano-electronic Devices and Systems. (Full time)”. In Section 2 of the application form you should insert the project title and name of the supervisor.

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online:

For further information please contact:


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