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Analysis of ambient marine noise through machine learning to coherently process distributed dense spatio-temporal profiles

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  • Full or part time
    Prof T Norman
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Supervisors: Mohammed Belal (NOC), 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 visualize these ambient noise fields.

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 machine learning and other artificial intelligence (AI) methods to start to address the challenges of localisation, tracking and discrimination of key events in the marine environment.

The supervision team for the PhD is:
• Dr Mohammed Belal, an expert in optoelectronics and distributed intelligent environmental monitoring,
• Dr Christine Evers, an expert in machine learning for acoustic signal understanding,
• Prof Tim Norman, an expert in AI and Machine Learning,

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.

Funding: full tuition for EU/UK Students plus, for UK/EU students resident in the UK, an enhanced stipend of £18,285, tax-free pa for 4 years. years.

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 3 April 2020 for entry in October 2020.

How To Apply

Applications should be made online Please enter Safe, flexible and explainable reinforcement learning for autonomous systems under the Topic or Field of Research.

Applications should include:
Research Proposal
Curriculum Vitae
Two reference letters
Degree Transcripts to date

For further information please contact: [Email Address Removed]

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

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