Supervisory Team: Rafael Mestre, Mohammad Belal, Tim Norman

Project description

This PhD project focuses on developing innovative techniques for event detection by exploiting Neurosymbolic Machine Learning methods to data acquired using distributed fibre optic sensing (DFOS) platforms. DFOS are becoming increasingly important for marine, maritime, glacial and urban environmental and infrastructure monitoring applications, in scenarios where environmental vibrations can be complex and challenging (seismic activity, underwater soundscapes, building’s structural integrity, traffic).

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.

If you wish to discuss any details of the project informally, please contact Dr Rafael Mestre, Agents, Interactions and Complexity (AIC) Research Group, Email: r.mestre@soton.ac.uk

UKRI MINDS CDT

This project is funded through the UKRI MINDS Centre for Doctoral Training (www.mindscdt.ai). 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 with a remit that 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.

Entry Requirements

We welcome applications from future experts who have, or expect to shortly have, a very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent), in computer science, electronics, engineering, or other closely related subject such as mathematics, physics or chemistry. Academic attainment is only one of our criteria for selection; we equally value excitement for research, enthusiasm for the research focus of the CDT and the ability to communicate ideas.

We are particularly keen to receive applications from people from a range of backgrounds and communities. ECS has recently been awarded the Athena SWAN Bronze Award recognising our commitment to fairness, inclusivity and gender equality.

EU and Overseas applicants should achieve an IELTS score of 6.5 with at least 6.0 in each competency.

Closing date: Applications should be received no later than 23 June 2023 but later applications may be considered depending on the funds remaining in place.

Funding: For UK students, Tuition Fees and a stipend of £21,110 tax-free per annum for 4 years.

How To Apply

Applications should be made online here.

Applications should include:

Research Proposal

Cover Letter

Curriculum Vitae

Two reference letters

Degree Transcripts to date

For further information please contact: mindscdt@soton.ac.uk or d.georgiadou@soton.ac.uk