Primary Supervisor - Prof Michal Mackiewicz

Scientific background

Marine litter is a key threat to the oceans health and the livelihoods. Hence, new scalable automated methods to collect and analyse data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category training dataset. However, there is a recognition of the need of multispectral imagery to enhance the accuracy of the algorithms being developed when discerning material type. Consequently, Cefas is developing a new lab to assist in characterisation of multispectral reflectance of materials.

Research methodology

The student will utilise the existing VL database of key materials, but importantly will also collect multispectral data with the enhanced lab setup with an aim to train the DL algorithms. Importantly, the algorithms developed must be robust to changing real-world illumination and utilised long-term, likely with imaging devices not existing during the development. This will require an approach that considers the physics of the multispectral image formation including the three key variables: sensor spectral sensitivities, varying daylight illumination spectrum and wide range of relevant material reflectance spectra.

Objectives

Develop a multispectral imaging dataset of marine litter materials by extending the existing VL dataset.

Design and evaluate DL models capable of classifying marine litter types using multispectral data, with a focus on achieving robustness to varying spectral channel configurations and illumination conditions.

Implement and validate device-independent representations. Investigate and apply domain adaptation and transfer learning techniques to develop models that generalize across different imaging devices, including future sensors with unknown spectral sensitivities.

Training

The student will be based at the Colour & Imaging Lab at the School of Computing Sciences which has expertise in the design and evaluation of imaging solutions and will have an opportunity to work with scientists and engineers at Cefas. They will undertake training specific to this project including imaging principles, lab measurement, computer vision and ArcGIS, potential fieldwork and UAV flying training.

Person specification

Experience and/or enthusiastic interest in one or more of the following areas: environmental monitoring, AI, computer vision or multispectral imaging.

Entry Requirements

At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5 overall, 6 in each category).

Acceptable first degree: Computer Science/Physics/Maths or other numerate discipline.

Mode of Study

Full-time

Start Date

1 October 2026

Funding Information

ARIES studentships are subject to UKRI terms and conditions . Successful candidates who meet UKRI's eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend (20,780 p.a. for 2025/26) and a research training and support grant (RTSG). A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. Please note, however, that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.