Job description
Supervisory Team: Mohammad Soorati Project Description: Studying the ocean and its inhabitants is an extremely challenging task: the animals and the physics that govern their habitat vary on an enormous range of spatiotemporal scales, and this renders monitoring from crewed ships infeasible. Scientists are increasingly relying on Autonomous Underwater Vehicles (AUVs) coupled with imaging systems to observe marine ecosystems. However, these tools are typically deployed to run a preset transect continually collecting data for later processing, removing opportunities to react in real-time to observed data. The potential for adaptive sampling on AUVs from image data is enormous, enabling creative sampling paradigms to better understand our changing oceans, enabling a single robot to stop and follow a new organism, or a system of robots could track and interrogate ephemeral biological features like thin layers of plankton. Such functionality would allow scientists to consider and study new ideas in biological oceanography. While embedded AI is becoming increasingly feasible to enable this, the associated power consumption has a significant impact on vehicle battery-life and hence the length of a mission. This project seeks to capitalise on advances in multimodal sensing and dynamic inference, to make on-board decisions to enable efficient and adaptive sampling/control based on real-time collected data. This will entail both processing the visual data itself and producing outputs that are actionable for mission planning. There are several interesting challenges to address and potential directions and PhD project opportunities. The project will explore both new techniques in computer vision to enable AI on the “edge” i.e. within AUVs, as well AI-enabled scheduling on-board the robot. In addition, techniques from swarm robotics and multi-agent reinforcement learning can be used to coordinate multiple AUVs where there is limited ability to communicate. This can furthermore be supplemented with static sensors from e.g. a crewed ship. Novel approaches need to be designed for path planning and to build a collective map of marine populations using a diverse set of vehicles and sensors. This is a 4-year integrated PhD (iPhD) programme and is part of the UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI). For more information about SustAI, please see: https://sustai.info/ Entry Requirements A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). Closing Date: 8th April 2024. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified. Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships | Doctoral College | University of Southampton. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered. How To Apply Apply online: HERE Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “iPhD Sustainable AI (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Mohammad Soorati. Applications should include: Research Proposal Curriculum Vitae Two reference letters Degree Transcripts/Certificates to date For further information please contact: feps-pgr-apply@soton.ac.uk
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Southampton, Hampshire
