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training programme by the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored
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equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills
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Eligibility Criteria You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc
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, biologists, and engineers. You will also have the opportunity to present your research at international conferences, expanding your network and gaining valuable experience in scientific communication
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in numerate, computational, or environmental subject areas. Numerate Subject Areas include e.g.: Mathematics, Statistics, Physics, Economics, Finance, Engineering (Mechanical, Electrical, Civil, etc
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networks which are intrinsically explainable; (5) design a new multi-dataset benchmark for assessing the trade-off between accuracy and explainability Project Description: Whereas traditional machine
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across the fern lifecycle (RT-PCR, qPCR). Test to see if these ancestral genes can function in male or female meiosis through genetic engineering, swapping out the Arabidopsis gene for the fern copy
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to a net zero economy, electrical power systems are rapidly decarbonizing supply whilst electrifying carbon-intensive demand. There is therefore an acceleration in the connection of low carbon
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Research theme: "Next Generation Wireless Networks", "Signal Processing", "Machine Learning" UK only How to apply: uom.link/pgr-apply-2425 This PhD project aims to design novel resource allocation
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap