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. The broad aim of the PhD research is to train the student with skills to investigate self-assemblies of lipids and biocides (cationic surfactants) and their cross-assembly processes, and identify how
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manufacturing (3D printing) techniques. The purpose of the studentship is to develop a next-generation in vitro model of aged human skin to evaluate the cytocompatibility of materials used in maxillofacial
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range of reactivity, and particularly C-C bond formation. Directed evolution approaches will then be applied to guide the selectivity of the new enzymes towards target molecules and drug fragments
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interference, while ensuring energy-efficient and scalable operation. This PhD project will focus on developing machine learning algorithms to enable robust channel estimation, intelligent user association
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in the felt structure, making traditional regeneration or disposal techniques both technically and environmentally inadequate. This PhD research seeks to redress this imbalance by developing scientific
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mechanical fatigue—individually and sequentially, followed by electrical breakdown testing to assess their impact on the dielectric performance of the material subsystems. The target is to develop a
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. Your work will feed directly into the development of predictive models that link microstructure to performance, guiding the design of alloys that are stronger, more reliable, and more efficient. By doing
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for the first time in 2024 after a 50-year search. The transition holds great potential as a next-generation clock, outperforming atomic clocks by orders of magnitude. A major goal is development of a
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-treatment facilities, and biorefineries. Feedstock choice, regional dynamics, and process side-streams all affect costs, energy use, and emissions. This PhD project will develop advanced computational models
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, spectroscopy, and battery testing equipment. You will also spend time working directly with Innospec, gaining valuable industrial experience and insight into catalyst development and applications. We are seeking