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Field
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to: build predictive spatial models for VME distribution and resilience under current and future climate scenarios; define and quantify functional traits of VME taxa; identify key functional groups, map
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areas, including generative modelling (e.g. diffusion models, flow matching, self-supervised and autoregressive approaches), causal machine learning, graph neural networks, dynamical systems modelling
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experimental structural / aerodynamic testing - Experience with wind turbine analysis and modelling methods Funding: 3.5 year Scholarship - minimum tax-free stipend at the current UKRI rate (for 2025/26
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flow characterisation Nanoparticle synthesis and drug delivery systems Cell culturing and in vitro biofilm modelling Collaborative research across engineering, chemistry, dentistry, and microbiology
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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of the modern age. What if there was another way? What if we could utilise the very land that lies at the foot of wind or solar farms? By pumping water into a sealed flexible membrane under a mass of soil we can
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during continuous shearing, which impacts wind turbine foundations, underground pipelines, pharmaceutical manufacture, among others. Sand is the most ubiquitous engineering material on earth, yet research
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composites To propagate uncertainty in material behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help
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their operational reliability. The PhD student will combine mathematical models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify
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I specialise in the numerical modelling of high-energy particle collisions , such as those occurring at the Large Hadron Collider. Accordingly, most projects I offer straddle the intersection