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PhD-studentship in Applied Mathematics / Quantitative Ecology: Wind‑Assisted Dispersal of Insect Tree Pests: An Interdisciplinary Modelling and Ecological Study Award Summary 100% home fees covered
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This interdisciplinary PhD project will bring together mathematics and ecology to assess the risk of invasive tree pests being transported into Great Britain by wind. While biosecurity measures largely focus on trade
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systems with "self-diagnosis" and "self-healing" capabilities. By integrating federated learning, graph neural networks, and blockchain technology, we will develop a framework that moves beyond static
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theory, Bayesian inference, Monte Carlo simulation, and statistical analysis of subjective data. Data science and machine learning - big data analytics, surrogate modelling, digital twin development, and
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minimum 2:1 Honours degree or international equivalent in a subject relevant to the proposed PhD project (e.g. Applied Mathematics, Physics, Mechanical or Chemical Engineering). Enthusiasm for research
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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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-culture studies to evaluate mechanotransductive signalling. The resulting data will inform mathematical models linking material mechanics to biological responses, enabling the sustainable design of next
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(mathematical) requirements and automated testing techniques can be used to produce clear, traceable, and regulator-ready evidence for medical device software certification. The research is part of
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Award summary 100% fees covered, and a minimum tax-free annual living allowance of £20,835 (2025/26 UKRI rate). Home Only Students. Overview Advanced Integrated Motor Drives – PhD Opportunity Electric drives are essential to the UK's net-zero ambitions and will play a crucial role in powering...
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application per studentship, you cannot apply for multiple studentships on one application. Contact Details Dr Adam Ingram School of Mathematics, Statistics and Physics Email: adam.ingram@newcastle.ac.uk