Use of AI and computer vision to develop next generation marine biological observing capability

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 unbiased 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 investigate current AI capability to deliver ecologically meaningful metrics from image-based data; and in so doing develop the methods and tools to support the wider application of AI to image-based biological observations.

    The student will use image datasets to explore the most effective methods in the application of AI to image analysis for biodiversity monitoring. Research will focus on understanding what AI is capable of in terms of delivering ecologically relevant data, and specifically robust measures of Essential Ocean Variables. The student will spend time at each of the three MRP partners working on different case study applications, including benthic ROV (UoP), pelagic iCPR (MBA), and pelagic APICS (PML) image data.

    The student will have a unique opportunity to expand their outlook into a highly multi-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, marine optics, R and Python programming.

  • Lead Supervisor (DoS): Professor Kerry Howell

    Second Supervisor: Dr James Clark

    Third Supervisor: Dr Pierre Hélaouët

  • Applicants should have a first or upper second class honours degree in an appropriate subject or a relevant Masters qualification. The ideal candidate will have a degree in either an ecological, computer science, or other highly numerate subject. Experience in mathematics, engineering, or similar is required. We recognise that candidates are unlikely to have both ecological and computer science skills, so candidates would preferrably have a strong programming background and a demonstrable capacity to learn new skills and adapt their knowledge to new situations.

    Non-native English speakers must have an IELTS Academic score of 6.5 or above (with no less than 5.5 in any element) or equivalent.

    The studentship is supported for 3.5 years and includes full Home tuition fees plus a stipend at the 2024/25 rate (to be confirmed; compare the 2023/24 rate of £18,110 per annum). The last 6 months of the four-year registration period is a self-funded ‘writing-up’ period. 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 at 2023/24 rate).

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

  • For further information and to apply for this position please visit https://www.plymouth.ac.uk/student-life/your-studies/research-degrees/postgraduate-research-studentships and select the studentship you would like to apply for. Please clearly state the name of the studentship that you are applying for on your personal statement.

    For a list of supporting documents to upload with your application or more information on the admissions process generally, please visit our How to Apply for a Research Degree webpage or contact the Doctoral College.

closing date for applications : 12 noon, Monday 08 January 2024