This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture Science), a global leader in marine science, the project will develop scalable, low-cost embedded vision systems to analyze marine biodiversity and detect anthropogenic debris. The core challenge is creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions.

The demand for such a low-cost system stems from the need to increase efficiency in marine monitoring. Furthermore, existing computer vision solutions often depend on cloud computing infrastructure and require specialized expertise. A scalable, embedded computer vision system that can analyze imagery in real time offers substantial value by enabling more accurate assessments of nearshore vegetation, shellfish stocks, anthropogenic debris, and epibenthic biodiversity.

The project will build on a working prototype, the Neural Network Enhanced Marine Observation system, a low-cost, shallow-water, edge-AI-enabled spatial camera system designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify benthic anthropogenic debris and monitor marine biodiversity.

94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021

Supervisors

• Primary supervisor: Qinggang Meng
• Secondary supervisors: Jon Hawes, Cefas, Peter Kohler.

Entry requirements

Applicants will normally need to hold, or expect to gain, at least a 2:1 degree (or equivalent) in Computer Science, Engineering, or an appropriate Master’s degree.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website .

Funding Notes

The NERC studentship is funded for 3.5 years starting from October 2025 and provides a tax-free stipend of £19,237 per annum (in 2024/25) for the duration of the studentship plus tuition fees at the UK rate. It also provides a Research Training Support Grant (RTSG) of £8,000. Further guidance about eligibility is available at UKRI Terms and Conditions . Due to UKRI funding rules , no more than 30% of the studentships funded by this grant can be awarded to International candidates, but successful International candidates will have the difference between the UK and International tuition fees provided by the University.

How to apply

1. Complete a CENTA studentship application form in Word format, available from the CENTA website .
2. All applications should be made online. Under programme name, select School of Science/Computer Science. During the online application process, upload the CENTA studentship application form as a supporting document. Please quote CENTA2025-LU8 when completing your online application.
3. Application closing date is midnight (UK time) on Wednesday 8 January 2025. Interviews for short-listed candidates are expected to be held in the period Monday 3 February – Friday 14 February 2025.

Apply now