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Field
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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to float over floodwaters. Recent work in behavioural biology emphasises how differences among individuals within groups (e.g. biomechanics, physiology, behaviour, cognition) determine group structure and
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the recording of substrate-borne vibrations produced by earthworms (and other invertebrates) in situ. To fully exploit this emerging technology there is a need to unravel the meaning within the data. As such
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to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking, and career development after the PhD. The faculty has outstanding facilities and works in partnership with
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materials. The computer modelling of LSP remains challenging due to its multi-physics and multi-scale nature. The dependency of the process on the shape of the laser pulse, its energy, ablation layers etc. is
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to maximize this impact throughout the PhD programme. Given a focus on body weight and strategies to reduce this, with consideration for sustainability and current consumer preferences for naturalness
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invites applications from candidates with a robust foundation in data science, modelling, and/or engineering, and a keen interest in deploying data analysis and artificial intelligence (AI) to solve real
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or social care related subject. Experience of software development and/or data visualisation are desirable but not essential. Funding notes: This PhD scholarship is available for home students only
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but not essential. A strong background in materials science and/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition
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PhD project (such as Electrical Engineering or Computer Science) is our standard entry, however we place value on prior experience, enthusiasm for research, and the ability to think and work