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Postgraduate research project

Neurosymbolic machine learning for distributed fibre optic sensing

Fully funded (UK only)
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

Distributed fibre optic sensing (DFOS) are becoming increasingly important for marine, maritime, glacial and urban environmental and infrastructure monitoring applications. They are important in scenarios where environmental vibrations can be complex and challenging such as seismic activity, underwater soundscapes, building’s structural integrity, traffic.

This PhD project focuses on developing innovative techniques for event detection. It will do so by using Neurosymbolic Machine Learning methods to exploit data acquired using DFOS platforms. 

Neurosymbolic machine learning is an exciting research area that combines the best of both symbolic and subsymbolic AI techniques. By integrating symbolic reasoning with deep learning, neurosymbolic approaches enable the development of models that are interpretable, explainable, and adaptable to new situations. You will have the opportunity to explore and contribute to this exciting field, opening up new avenues for research in event detection and beyond.

You will be supervised by a team of interdisciplinary researchers in machine learning, signal processing and distributed systems, and will have the opportunity to collaborate with industry partners to further your research. You will join the School of Electronics and Computer Science in collaboration with the National Oceanography Centre (NOC) and will have professional development opportunities through the Alan Turing Institute, the UKRI TAS Hub, and access to Future Worlds to explore commercialization for your research.

This project is funded through the UKRI MINDS Centre for Doctoral Training. This is one of 16 PhD training centres in the UK with a unique focus on advancing AI techniques in the context of real-world engineered systems. Its remit spans novel hardware for AI, AI and machine learning, pervasive systems and IoT, and human-AI collaboration. We provide enhanced cross-disciplinary training in electronics and AI, entrepreneurship, responsible research and innovation, communication strategies, outreach and impact development as part of an integrated 4-year iPhD programme. 

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