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Integrated Lab-on-Chip system for Autonomous Underwater Vehicle using optical sensors and deep learning

   School of Engineering and the Built Environment (SEBE)

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  Dr A Kerrouche, Prof H Yu  No more applications being accepted  Self-Funded PhD Students Only

About the Project

The increasing need of protein for a growing world population puts the aquaculture industry under pressure to expand and increase production. It is predicted that 60% of seafood in the UK will come from aquaculture, due to demands and limitations in capture fisheries. The industry is already threatened by fish mortalities related to naturally occurring parasites, pathogens and diseases. Novel methods for monitoring the quality of water in aquaculture are required, as samples need to be collected from several locations and transported to the laboratory within a short period of time. Most systems used today are expensive, slow and labour intensive. Robotic, automated systems for pathogen detection can greatly reduce cost per sample and extend sample numbers, area and density. The aim of this project is to propose, design, built and test an Autonomous Underwater Vehicle (AUV) for pathogen detection, equipped with an innovative solution for sample collection and analyses. In addition, the results of the on-board pathogen detection system would be communicated wirelessly to a ‘Cloud’ based server. The proposed system would enhance the ability to monitor and detect the presence of pathogens allowing aquaculture companies to act prompt and ensure public safety. The miniaturised lab on an AUV system could change monitoring strategies for diseases related to pathogens by providing on-site analysis

capability, without the need for subjective microscopy for identification, subsequently reducing costs and process time significantly.

Academic qualifications

A first degree (at least a 2.1) ideally in Electronic/Optical Engineering, Computer Science, Bioinformatics with a good fundamental knowledge of Image processing and bioinformatics.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

· Experience of fundamental Strong machine learning background

· Competent in Matlab, Python, Java and/or C++

· Knowledge of Biomedical Optics

· Good written and oral communication skills

· Strong motivation, with evidence of independent research skills relevant to the project

· Good time management

Desirable attributes:

Practical research expertise in an optical lab and/or a clean-room environment

Funding Notes

Self funded


Abdelfateh Kerrouche , MP Desmulliez and H. Bridle “Megasonic sonication for cost-effective and automatable elution of Cryptosporidium from filters and membranes”. J Microbiol Methods. 2015 Nov;118:123-7
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