DoS: Kerry Howell (Email: [email protected] )
2nd Supervisor: Dena Bazazian (Email: [email protected] )
3rd Supervisor: Lucy Turner (Email: [email protected] )
4th Supervisor: Mark Briffa (Email: [email protected] )
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.
Project Description
Scientific background:
Imaging platforms are now a key tool in the assessment and monitoring of marine biodiversity. Examples include the use of ariel 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.
Research methodology:
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, ariel drone survey of land crab populations, and laboratory observations of anemone behaviour.
Training:
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.
Person specification:
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.
If you wish to discuss this project further informally, please contact Professor Kerry Howell, [email protected]
For further information on Eligibility and Funding, please click on the links below:
To apply for this position please click the 'Apply' button, above.
Please clearly state the name of the DoS and the studentship project that you are applying for on the top of your personal statement.
Please see here 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 .
The closing date for applications on 26 April 2024. 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.