Improving autonomous platforms for next generation biodiversity observations

(HOWELL_P24ARIES)

Improving autonomous platforms for next generation biodiversity observations

(HOWELL_P24ARIES)

Project Description

Supervisors

Professor Kerry Howell, University of Plymouth – Contact me

Dr David Moffat, Plymouth Marine Laboratory

Professor Alex Nimmo-Smith, SoBMS, University of Plymouth

Dr Dena Bazazian, School of Engineering, Computing, and Mathematics. University of Plymouth

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.

References

  • 1 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
  • 2 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.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.
  • 3 Bazazian, D., Magland, B., Grimm, C., Chambers, E., & Leonard, K. (2022) Perceptually grounded quantification of 2D shape complexity. The Visual Computer, 1-13.
  • 4 Ferreira AS, Costa M, Py F, Pinto J, Silva MA, Nimmo-Smith A, Johansen TA, de Sousa JB & Rajan K (2019) 'Advancing multi-vehicle deployments in oceanographic field experiments' Autonomous Robots 43, (6) 1555-1574
  • 5 Venkatesh, S., Moffat, D. and Miranda, E.R., (2022). You only hear once: a yolo-like algorithm for audio segmentation and sound event detection. Applied Sciences, 12(7), p.3293.

Key Information

  • This project has been shortlisted for funding by the ARIES NERC DTP and will start on 1st October 2024. The closing date for applications is 23:59 on 10th January 2024.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship, which covers fees, stipend (£18,622 p.a. for 2023/24) and research funding. International applicants are eligible for fully-funded ARIES studentships including fees. Please note however that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK. We expect to award between 4 and 6 studentships to international candidates in 2024.
  • ARIES students benefit from bespoke graduate training and ARIES provides £2,500 to every student for access to external training, travel and conferences, on top of all Research Costs associated with the project. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.
  • ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside non-academic experience, and our recruitment process considers potential with the same weighting as past experience.
  • All ARIES studentships may be undertaken on a part-time or full-time basis, visa requirements notwithstanding.
  • For further information, please contact the supervisor. To apply for this Studentship follow the instructions at the bottom of the page or click the 'apply now' link.
  • ARIES is required by our funders to collect Equality and Diversity Information from all of our applicants. The information you provide will be used solely for monitoring and statistical purposes; it will remain confidential, and will be stored on the UEA sharepoint server. Data will not be shared with those involved in making decisions on the award of Studentships, and will have no influence on the success of your application. It will only be shared outside of this group in an anonymised and aggregated form. You will be ask to complete the form by the University to which you apply.
  • ARIES funding is subject to UKRI terms and conditions. Postgraduate Researchers are expected to live within reasonable distance of their host organisation for the duration of their studentship. See https://www.ukri.org/publications/terms-and-conditions-for-training-funding/ for more information

Applications open

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