Uncrewed submarines, underwater gliders, and surface vessels are the future of environmental monitoring and naval surveillance in the ocean. These autonomous robots will cooperate with and, in many cases, replace humans (reducing danger to life) to give us a wide-reaching and persistent view of our underwater environment. However, there is "an elephant in the room": artificial intelligence (AI) consumes power (potentially a lot of power!) and marine robots have a limited supply, e.g., from batteries or renewable sources such as solar. Therefore, there is a critical engineering need for low-power AI algorithms and hardware for this vision to become a practical reality.
This PhD will explore the trade-off between AI capability and reliability versus the realistic power limitations imposed on robots in the challenging real-world ocean environment. Furthermore, it will develop, test, and demonstrate low-power AI solutions on a real marine robot. The project is co-funded by the Seiche Water Technology Group and will be centred around their Autonaut wave-propelled, solar powered uncrewed surface vessel (USV) with its underwater acoustic and above-water electro-optic sensors. Relevant use cases include monitoring of marine mammals, oceanographic science, and naval surveillance for underwater intrusion into protected areas.
The project is supported under the UKRI Centre for Doctoral Training (CDT) in Accountable, Responsible, and Transparent AI (ART-AI) within a multi-disciplinary cohort from engineering, computer science, and social science. The primary focus will be on a feasible, reliable, and safe engineering application of AI. To this end, it will be contributing to machine learning innovations in computer science and engineering. However, it will also be guided by marine law, policy, and ethics from the social sciences, with important research questions relating to trade-offs in the "power budget" for: technical capability and performance; transparency and understandability for effective human cooperation, trust, and accountability; and governance to ensure safe and responsible operation.
Applicants should hold, or expect to receive, a first or upper second class honours degree in a relevant subject. A master’s level qualification and the ability to gain UK Security Check clearance or meet the Baseline Personnel Security Standard would be advantageous (for which there are nationality rules). Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.
Informal enquiries about the project should be directed to Dr Alan Hunter: email@example.com.
Formal applications should be made via the University of Bath’s online application form and be accompanied by a research proposal. Enquiries about the application process should be sent to firstname.lastname@example.org.
Start date: 4 October 2021.
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