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PhD Studentship: Artificial Intelligence for Building Performance – Optimising Low-Pressure Airtightness Testing Supervisors: Dr Christopher Wood (Faculty of Engineering) and Dr Grazziela Figueredo
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planned acts, such as targeted terrorist activities. This exciting project will focus on developing a mathematical model (and a supporting research software) to predict the key performance indicators of a
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-edge advancements in automated drug discovery and bio-instructive material manufacture. The project aims to utilise flower waste as a sustainable feedstock to discover new bioactive small molecules, then
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with a rigorous approach to research. Applicants should have, or be expected to gain a high 2:1, preferably a 1st class honours degree in Chemical or Mechanical Engineering or Chemistry or a related
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optical setup construction. They will make use of commercial simulation software to test electromagnetic designs, algorithmic coding to design metamaterials, fabrication techniques to produce
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optical setup construction. They will make use of commercial simulation software to test electromagnetic designs, algorithmic coding to design metamaterials, fabrication techniques to produce
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Manufacturing research group (CfAM). The student will work in world-class laboratory facilities in the CfAM engaging with interdisciplinary team with expertise in 3D printing, biotechnology, physics, and
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manufactured using plasma spray and mini-combustion flame spray and examined in SEM/EBSD and tested in a burner rig and furnace thermal cycling. As a part of a CDT cohort, you will have access to tailored
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partners and test centres. Physical testing, such as controlled spin-burst experiments, will complement advanced finite element analysis (FEA) in evaluating failure behaviour. Who we are looking
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addressing the behaviour of thin foil materials for aerospace forming applications. The successful candidate will have a first-class or upper second-class honours degree in mechanical engineering or a related