Publisher
source

Robert Ellis

3 months ago

PhD Studentship – Harnessing Mussel Behaviour and Machine Learning for Coastal Water Quality Monitoring University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

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Country

United Kingdom

University

University of Exeter

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Where to contact

Official Email

Keywords

Computer Science
Environmental Science
Biology
Aquaculture
Sensor Technology
Embedded Software
Biosensors
Marineecology
Wireless Communications
Machinelearning
Harmful Algae
Environmental Monitoring And Assessment

About this position

[£20,780 per year plus payment of tuition fees (Home rate) and a Research Training Support Grant of £5,000 over 3.5 years.]

The University of Exeter invites applications for a fully funded PhD studentship focused on developing next-generation environmental biosensors by harnessing mussel behaviour and machine learning for coastal water quality monitoring. This interdisciplinary project is based at the Streatham Campus and addresses the urgent need for real-time, deployable solutions to monitor and protect marine ecosystems threatened by pollution, eutrophication, and harmful algal blooms (HABs).

Bivalves such as mussels are highly sensitive to changes in water quality, exhibiting distinct valve gape behaviours in response to environmental stressors like oxygen depletion, toxic algae, and pollutants. This project aims to transform these natural responses into actionable environmental intelligence by advancing both sensor engineering and behavioural data interpretation. Building on recent innovations at Exeter, the research will translate a novel discrete gape-sensor unit from laboratory and short-term field studies into a fully integrated, real-world monitoring system. The system will feature automated live analysis, integrating machine learning algorithms to interpret complex mussel behavioural patterns and generate real-time alerts for rapid response to pollution events or HABs.

The technical innovation lies in combining robust, low-power hall-sensor hardware with wireless communication and advanced analytical software. The project will train machine learning models to distinguish between normal physiological behaviours (such as diurnal rhythms and feeding) and abnormal, stress-induced patterns. This requires expertise in biosciences to characterise mussel responses under controlled exposures, and engineering to design hardware, firmware, and analytical pipelines capable of autonomous field operation.

The societal and industrial impact of this research is significant. Coastal communities and aquaculture industries are vulnerable to HABs and pollution events that can cause mass mortalities, economic loss, and health risks. A low-cost, deployable sensor network based on mussel behaviour could provide real-time environmental intelligence, supporting regulatory agencies, aquaculture managers, and marine spatial planners. As climate change increases the frequency and intensity of HABs, such proactive management tools are increasingly vital.

The project is delivered through multidisciplinary collaboration. Dr Robert Ellis (Biosciences) provides expertise in bivalve physiology and aquaculture, ensuring robust experimental validation. Dr Jun Chew (Engineering) leads on sensor design, system integration, and embedded software development. Industrial engagement is provided by Prof Mike Allen and SeaGen, a blue-tech company supporting product development and commercialisation strategies. This supervisory team offers a unique environment bridging fundamental biology, applied engineering, and industrial innovation.

Funding covers a stipend of £20,780 per year, full tuition fees (Home rate), and a Research Training Support Grant of £5,000 over 3.5 years. Applicants should have a strong background in biosciences, environmental science, engineering, or computer science, and an interest in machine learning, sensor technology, or aquatic biology. English language requirements apply. For project-specific enquiries, contact Dr Robert Ellis at [email protected]. Apply online by 12 January 2025 via the University of Exeter portal.

Funding details

Available

What's required

Applicants should meet the entry requirements for a PhD programme at the University of Exeter, typically including a good undergraduate degree (2:1 or above) or equivalent in a relevant subject such as biosciences, environmental science, engineering, or computer science. Experience or interest in machine learning, sensor technology, or aquatic biology is desirable. English language proficiency requirements must be met if applicable. Please consult the University of Exeter's PhD admissions page for full details.

How to apply

Apply online via the University of Exeter application portal using the provided link. Ensure you meet the entry requirements for the relevant PhD programme. Direct project-specific enquiries to Dr Robert Ellis at [email protected]. Complete your application before the deadline of 12 January 2025.

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