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PhD Studentship: PhD Marine Sciences - “Improving Autonomous Platforms for Next Generation Biodiversity Observations"

University of Plymouth

Qualification Type: PhD
Location: Devon, Plymouth
Funding for: UK Students, EU Students, International Students
Funding amount: The studentship is supported for 3.5 years and includes a stipend from £18,622 per annum (2023-24 rate)
Hours: Full Time
Placed On: 30th October 2023
Closes: 10th January 2024

Improving autonomous platforms for next generation biodiversity observations

DoS: Professor Kerry Howell (kerry.howell@plymouth.ac.uk)

2nd Supervisor: Dr David Moffat, Plymouth Marine Laboratory (dmof@pml.ac.uk)

3rd Supervisor: Professor Alex Nimmo-Smith (alex.nimmo.smith@plymouth.ac.uk)

4th Supervisor: Dr Dena Bazazian (dena.bazazian@plymouth.ac.uk)

Applications are invited for a 3.5 years PhD studentship. The studentship will start on 01 October 2024

Project Description

Scientific background:

Predicting how ocean life will respond to pressures from increasing human use and climate change is the basis for science-informed decision-making. It requires development of models that enable forecasting of possible outcomes in 'what if' scenarios. Such models demand large un-bias biological ‘training’ datasets, which are difficult and expensive to collect and analyse using current human-reliant methods. Greater automation in collection and analysis of observations is needed to deliver sufficiently large datasets to significantly enhance our predictive modelling capability. In this respect Artificial Intelligence (AI) is a potentially powerful tool. This studentship will develop next generation marine biological observing capability by combining vision-enabled smart autonomous platforms with state-of-the-art machine learning.

Research methodology:

The student will collect new image-based observation data from Plymouth Sound National Marine Park using the University (and potentially PML’s) small AUVs. They will explore image enhancement methods to improve the quality and consistency of imagery, before annotating these data, providing both baseline observations for the NMP and a high-quality human-annotated image dataset for use in training AI algorithms to identify and quantify coastal benthic animals. The student will train deep-learning algorithms to identify coastal benthic marine taxa from AUV imagery, and trial real-time automated biological monitoring in coastal environments.

Training:

The student will have a unique opportunity to expand their outlook into a highly multi-disciplinary domain. They will interact with ecologists, computer scientists, and engineers, 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, marine optics, R and Python programming. The student will spend periods of time at PML and thus will benefit from interaction with 2 institutions.

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, kerry.howell@plymouth.ac.uk .

Eligibility and Funding

For further information on Eligibility and Funding, please click on the links below: 

To apply for this position please visit here.

Please clearly state the name of the studentship 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 10th January 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.

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