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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms
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Discover your career The world of the University of Nottingham is defined by our people and the values we share. Our environment is an ambitious vision brought to life across vibrant and forward-thinking global campuses. An ever changing world where open minds and diverse cultures are able to...
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In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and machine learning algorithms and to assess when AI predictions are likely to be correct and when,...
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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behaviours of thin foils in vacuum and inert environments will be explored. Based on the results, a constitutive material model including the creep effect (time, temperature and load dependencies) will be
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modelling. This exciting project involves the application of innovative methods such as high-throughput experimentation to expediate the syntheses (and bioanalysis) of life-saving pharmaceuticals
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models used in component scale CFD assessments. In particular, this concerns quantifying the effects of the heat transfer surface’s detailed topography, porosity and wettability on near-wall bubble
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-frequency Joule losses. Litz wire is one of the most promising solutions due to its exceptional ability to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully
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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