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atmosphere (1). Research Methodology: The primary objective of this PhD studentship is to develop near real-time capability for top-down ffCO2 quantification, based on synchronous changes in atmospheric CO2
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This 3.5-year PhD studentship is open to Home (UK) applicants and EU students with settled status. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for
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This 3.5-year PhD studentship is open to Home (UK) applicants. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject to annual uplift
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, engineers, and the general public Qualifications Research Assistant Degree in engineering or numerate subject (e.g., mathematics, physical sciences, computer science) PhD close to completion in field of Power
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Research Infrastructure? No Offer Description Two fully-funded 3-year PhD studentships are available in Neuromorphic and Bio-inspired computing at the interface between control engineering, electrical
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Deadline: All year round How to apply: https://uom.link/pgr-apply-2425 UK only This 3.5-year PhD project is open to home students; the successful candidate will receive an annual tax free stipend
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
We are pleased to announce a self-funded PhD opportunity for Quantitative assessment of damage in composite materials due to high velocity impacts using AI techniques. Composite materials, such as
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, multidimensional datasets is transforming marine ecology and redefining how we detect and respond to ecosystem change. Methodology This PhD will place you at the forefront of this emerging field. You will address a
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a transitional trait that can act as a “safety net” between hybrid formation and genomic stabilisation. The evolutionary implications of apomixis remain unresolved despite its widespread occurrence
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural