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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
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-derived criteria. A scoping review, alongside initial semi-structured interviews and focus group transcripts subjected to thematic analysis, will build a picture of what key stakeholders want. Clinic
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regulations and product certification due to the inherent uncertainty of how AI systems make decisions. Classical engineering development guidelines, are difficult to interpret or simply not transferrable to AI
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academic team in the newly built Dalton Building with state-of-the-art facilities. Working with an industrial partner will hugely enhance the students’ capability to apply theoretical knowledge to different
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installations. Building on these research and industrial successes, this project aims to design soft robotic systems capable of fast locomotion within aero-engines and operating end effectors for non-destructive
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a particular focus on robotic control. Building from existing concepts on continual learning, this project will develop new frameworks for training and maintaining robustness and stability of deep
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n-type ceramic thin films. These materials will be processed at low temperatures on form-free substrates, with an emphasis on their integration into affordable, wearable healthcare devices. Building
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for future aircraft applications. Together we will make technological advances that will lead to more sustainable and safe air travel. Motivation The aviation industry is responsible for 12% of carbon dioxide
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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have experience in one or more of these subject areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. What you would be doing: You will be creating data