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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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they operate within. Skills Domains During their studies students will train across five skills domains. Responsible NLP data and models Students will develop techniques for building and curating inclusive and
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. The aim of this project is to determine how ppRHOs underpin this ecosystem service. Thus, the PhD candidate will physiologically characterise ppRHO knock-in diatom cell lines and investigate how those
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This PhD project aims to achieve a breakthrough in thermal efficiency (>50%) for dual-piston Free-Piston Engine Generators (FPEGs). The research will pioneer a novel, integrated methodology
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1-dimensional ocean-ice model. You will identify any long-term changes in ocean CO2 uptake along the Antarctic Peninsula using data from Rothera, SOCAT (www.socat.info) and mapped CO2 products, while
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to explore bumblebee behaviour, navigation and landscape-use to better understand insect cognition. The overall grant covers microbattery development; antenna design; field experiments and cognitive modelling
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this fundamental challenge, the PhD candidate will be part of a wider team to establish methodological framework, combing utilisation of controlled tree growth test, thermodynamic modelling and advanced optical
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start dates also possible) Email: david.duncan@nottingham.ac.uk About the project We are recruiting applicants for a fully funded PhD studentship in the School of Chemistry at the University of Nottingham
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, understand, characterise, and model the effect of surface roughness on wall turbulence when out of equilibrium. Particular emphasis will be made on determining if any form of flow similarity exists across
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extending the existing VL dataset. Design and evaluate DL models capable of classifying marine litter types using multispectral data, with a focus on achieving robustness to varying spectral channel