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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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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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, Physiology and Neuroscience (PPN) in the School of Life Sciences at the University of Nottingham. The role will explore the molecular pharmacology of receptor tyrosine kinases (RTKs), focusing on how
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determine the impact of community acquired pneumonia that requires hospitalisation has on the quality of life of patients. The final stage will be to design a generic economic model to evaluate any new
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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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skills include: Interest or background in composite materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a
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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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: Investigating how functional motifs are encoded in HS chains and how they influence their biological activity. Using gastruloids as a model system with which to study GAG structure/function relationships