Monitoring marine biodiversity using AI approaches

Director of Studies: Professor Kerry Howell
2nd Supervisor: Dr Dena Bazazian
3rd Supervisor: Dr Lucy Turner
4th Supervisor: Professor Mark Briffa 

Applications are invited for a 3.5 years PhD studentship within the Environmental Intelligence doctoral training programme at the University of Plymouth. The studentship will start on 01 October 2024. 

Imaging platforms are now a key tool in the assessment and monitoring of marine biodiversity. Examples include the use of aerial drones to monitor shores and sea surface populations, use of AUVs and ROVs to survey benthic populations, and the use of static cameras to record behaviours. Processing imagery to extract biologically relevant information is challenging and to date has largely relied on the use of human effort to extract information on identity, abundance, and behaviour of animals. Other key information could be extracted from imagery, for example size-based information (biomass, volume) but this is rarely undertaken due to the technological difficulty, despite biomass being considered as an Essential Ocean Variable. Artificial intelligence and 3D modelling has the potential to significantly advance our capability to monitor marine biodiversity autonomously using imaging platforms, but reliable and integrated workflows to extract information need to be developed and demonstrated. 

The student will use existing and novel AI based approaches, to develop and demonstrate new methods of retrieving quantitative data on species from imagery including video. They will focus their research on three use case studies: ROV survey of deep-sea coral and sponge species, aerial drone survey of land crab populations, and laboratory observations of anemone behaviour.

The student will have a unique opportunity to expand their outlook into an inter-disciplinary domain. They will interact with both ecologists and computer scientists, developing a wide network beyond the supervisory team. Depending on their background the student may receive training in ecology and taxonomy, artificial intelligence and deep-learning, R and Python programming.

A degree in either an ecological field, computer science field, or other highly numerate field e.g. mathematics, engineering, etc is required. We recognise that candidates are unlikely to have both ecological and computer science skills. Thus, we are looking for someone with a strong programming background and a demonstrable capacity to learn new skills and adapt their knowledge to new situations.

Piechaud N & Howell KL (2022) 'Fast and accurate mapping of fine scale abundance of a VME in the deep sea with computer vision' Ecological Informatics 71

Piechaud N, Hunt C, Culverhouse PF, Foster NL & Howell KL (2019) 'Automated identification of benthic epifauna with computer vision' Marine Ecology Progress Series 615, 15-30.

Bazazian, D., Magland, B., Grimm, C., Chambers, E., & Leonard, K. (2022) Perceptually grounded quantification of 2D shape complexity. The Visual Computer, 1-13. 

Applicants should have a first or upper second-class honours degree in a science subject or a relevant masters qualification. 

If your first language is not English, you will need to meet the minimum English requirements for the programme, IELTS Academic score of 6.5 (with no less than 5.5 in each component test area) or equivalent. 

The studentship is supported for 3.5 years and includes full home tuition fees plus a stipend of £19,088 2024/25 rate (TBC). The studentship will only fully fund those applicants who are eligible for home fees with relevant qualifications.  Applicants normally required to cover international fees will have to cover the difference between the home and the international tuition fee rates approximately £12,697 per annum 2023/24 rate (2024/25 rate TBC).

NB: The studentship is supported for 3.5 years of the four-year registration period. The subsequent 6 months of registration is a self-funded ‘writing-up’ period.

If you wish to discuss this project further informally, please contact Professor Kerry Howell.

Please see our how to apply for a research degree page for a list of supporting documents to upload with your application.

For more information on the admissions process generally, please visit our how to apply for a research degree webpage or contact The Doctoral College at research.degree.admissions@plymouth.ac.uk.

Shortlisted candidates will be invited for interview after the deadline. We regret that we may not be able to respond to all applications.  Applicants who have not received a response within six weeks of the closing date should consider their application has been unsuccessful on this occasion.