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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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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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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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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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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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this by monitoring infectious disease trends and outbreaks using systems that collect information over time and allow unusual changes to be detected. While these normally count laboratory test results
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on modelling and testing of new reactors with a view to optimising the best systems for mixing supercritical water (>378o C and 221 bar) with wastewater feed streams. This needs to generate residence times
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to characterise the material behaviours of thin sheets and foils (tensile, shear and creep effects) to improve the forming process and optimise process variables. Experimental methods for testing mechanical
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and a construction and testing capability up to 5MW Vision This project aims to leverage the electric propulsion hardware developed in the EU-funded €40M NEWBORN – “NExt generation high poWer fuel cells
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Manufacturing process for Cold Spray with Artificial Intelligence, operate the AM machine, characterise the materials with scanning electron microscopy and transmission electron microscopy with tensile testing