Project Description
The health of our oceans is critical to the planet’s overall environmental stability, yet marine biodiversity is under increasing threat from climate change, overfishing, and pollution. Traditional methods of monitoring underwater ecosystems are often limited by the challenges of the marine environment, such as difficult access and poor visibility. There is an urgent need for innovative approaches that can provide accurate, real-time biodiversity data. This project seeks to harness Artificial Intelligence (AI) and advanced computer vision to transform underwater monitoring. Automating species identification and behaviour analysis will improve the quality and efficiency of biodiversity assessments, supporting the conservation and sustainable management of marine resources.
The candidate will engage in groundbreaking research at the crossroads of AI, computer vision, and marine biology, working in state-of-the-art facilities at all three Marine Research Plymouth institutions. The student will develop and refine AI models to detect, classify, and analyse marine species from underwater imagery and video. This work will involve processing data from various sources, including remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and fixed underwater cameras. The candidate will have opportunities for fieldwork to validate the models on diverse marine environments.
This project offers extensive training in AI, machine learning, and computer vision, with a focus on their application in marine biology. The student will gain proficiency in programming languages like Python and will work with AI frameworks such as TensorFlow or PyTorch. Additionally, the candidate will learn advanced techniques in marine data collection and analysis, providing comprehensive skills set that span both computational and ecological domains. The project also includes opportunities for collaboration with international research teams and attendance at leading conferences in both fields of AI and marine science.