PhD Studentship - Living Sensors: Harnessing Mussel Behaviour and Machine Learning for Coastal Water Quality Monitoring (University of Exeter)

Job title

PhD Studentship - Living Sensors: Harnessing Mussel Behaviour and Machine Learning for Coastal Water Quality Monitoring.

Company

University of Exeter - Department of Biosciences.

Description of the job

The University of Exeter offers a fully funded EPSRC Doctoral Landscape Award studentship for a PhD project developing next-generation environmental biosensors using mussels as living sensors for coastal water quality monitoring. The health of marine ecosystems is increasingly threatened by pollution, eutrophication, and harmful algal blooms (HABs), which have profound ecological and socio-economic impacts.

This PhD project aims to transform an existing prototype technology into a deployable environmental monitoring solution. Building on recent innovation at Exeter, the project will translate a novel discrete gape-sensor unit into a fully integrated system for real-world deployment, where rapid detection of water quality deterioration is critical. The research will integrate machine learning algorithms capable of interpreting complex behavioural patterns of mussels in response to environmental stress, enabling generation of real-time alerts and warnings.

The technical innovation lies in combining robust low-power hall-sensor hardware with wireless communication and complex analytical software. The project requires interdisciplinary expertise: biosciences to characterise the physiological responses of mussels under controlled exposures, and engineering to design hardware, firmware, and analytical pipelines that can run autonomously in the field.

The societal and industrial value is significant, as coastal communities and industries such as aquaculture are vulnerable to HABs and pollution events that can cause mass mortalities, economic loss, and human health risks. The project offers collaboration with SeaGen, a blue-tech company, providing industrial engagement and route-to-market strategies.

Key aspects of the project:

• Develop automated live analysis integrating machine learning algorithms for interpreting mussel behavioural patterns.
• Train machine learning models to distinguish between normal physiological behaviour and abnormal stress-induced patterns.
• Design robust low-power hall-sensor hardware with wireless communication capabilities.
• Create a low-cost, deployable sensor network for real-time environmental intelligence.
• Work in multidisciplinary collaboration spanning biosciences, engineering, and industry.
• Develop both fundamental insights into bivalve behavioural ecology and a technological platform for environmental monitoring.

Requirements

Essential requirements:

• A strong undergraduate or Master's degree (2:1 or above, or equivalent) in Engineering (electronic engineering, robotics, sensor development) or Computer/Data Science (machine learning, embedded systems, AI applications).
• Demonstrated ability to work across disciplinary boundaries and willingness to learn new skills outside core background.
• Strong analytical and problem-solving skills.
• Good communication skills and ability to work independently and as part of a diverse, multidisciplinary team.

Desirable skills/experience:

• For engineering candidates: experience in hardware development (sensor systems, microcontrollers, IoT devices) or signal processing.
• For computer science/data candidates: prior experience with machine learning, time-series analysis, or data-driven behavioural modelling.
• Familiarity with marine or environmental monitoring applications.
• Experience in experimental design, data handling, or statistical analysis.
• Interest in technology transfer, applied research, and industry collaboration.

Location

Hatherly, Streatham Campus, Exeter, Devon, UK.

Salary

For successful eligible applicants, the studentship comprises:

• An index-linked stipend for up to 3.5 years full-time (currently £20,780 per annum for 2026/27 rate).
• Pro-rata for part-time students Payment of University tuition fees (Home).
• Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students.

How to apply

Apply via the departmental application link. Submit: Personal Statement, CV, Transcripts, Two references. References must be emailed to pgrapplicants@exeter.ac.uk by 12th January 2026.

Contact primary supervisor: r.p.ellis@exeter.ac.uk for project-specific enquiries.

Deadline

12/01/2026.

Disclaimer

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