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Intelligent Sensor Perception to Improve the Autonomy of Unmanned Aerial Systems (PHDCEPS1920006)

School of Engineering & Computing

About the Project

University of the West of Scotland are leading the €5 million European Commission H2020 RAPID project (Risk Aware Port Inspection Drones), which will extend drones to autonomously monitor maritime transport infrastructures such as bridges, ports, and ships. The studentship is fully funded by RAPID and will be embedded within the Advanced Laboratory for Manufacturing and Autonomous Digital Applications (ALMADA) at the UWS Lanarkshire campus. In addition to the ALMADA research team, project partners in RAPID include Thales, SINTEF, Fraunhofer, Port of Hamburg, XOcean, Revolve Water, University of Limerick and University of Dundee. There will be inter-disciplinary collaboration among partners and with external stakeholders such as the European Union Aviation Safety Agency.

The PhD researcher on the RAPID project will work across branches of artificial intelligence, machine vision, perception sensor modelling, and high-performance virtual reality systems. The RAPID use cases focus on improving the safety of transport system infrastructure in ports and harbours, such as road/rail bridges and ship hull inspection. Currently, one in ten bridges across the developed world is classified as at risk of collapse as many near the end of their design life and current inspection survey methods are exceedingly labour and resource-intensive. The aim of RAPID is to use a combination of intelligent aerial and maritime drone swarms to continuously assess the structural condition of large critical infrastructure at scale and at greatly reduced cost. The goal is to save lives through early detection of structural failures. To meet this goal, the PhD research objectives will focus extending the capabilities of drone sensor perception to optimise the risk management of flight beyond visual line of sight and in hazardous situations such as in close quarters to complex infrastructure (to enable highly detailed surface imaging). The PhD candidate will develop innovative artificial intelligence algorithms for self-optimisation of LIDAR sensor perception, extend virtual reality scenario simulation to enable mission profiling and hazard mitigation, and exploit massively parallel GPU compute frameworks for validation of sensor-driven machine vision and drone context awareness systems.

The successful candidate will have obtained a First Class or Upper Second Class honours degree or Masters in a relevant technology or engineering discipline (Robotics/Software/Electronics). Applicants are expected to demonstrate excellent technical skills in high-performance software development (preferable Python, C++, CUDA / SYCL, and Matlab / Octave), with particular importance given to expertise in design and development of algorithms for high performance and/or resource-constrained use cases. Applicants should have experience in cloud technologies for machine learning and artificial intelligence, especially for big data or sensor-driven applications. The successful candidate will be expected to deliver high-quality production-ready source code and expertise in software development models, toolchains, and life cycle best practices is a pre-requisite. Knowledge and understanding of research and project management methodologies is desirable. Excellent verbal and written communication skills in English are a must.

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