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Field
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with
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water, etc.) by fusing sensor data (e.g. sonar, INS, USBL). Collaborative control strategies: Developing coordinated control algorithms that allow the USV and UUV to perform joint missions without
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout
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detailed local inspection. Digital twin simulations: Developing simulation environments replicating sensor characteristics and anomaly conditions to test perception algorithms under controlled, repeatable
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scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest ML tools and frameworks
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input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of
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safety. You will work on developing control algorithms all the way to performance assessment in test vehicles. The project combines theoretical aspects of control algorithms, experimental design, and
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of advanced computational techniques. This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic
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operating (Waterstromen) membrane-based wastewater treatment plants. As part of the UT team, you will develop a robust model predictive control (MPC) algorithm based on sensor and other system inputs that can