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PhD Studentships - Process Industries: Net Zero - Chemical Engineering for Sustainable Wastewater-to-Resource Systems Award Summary 100% fees (UK Home only), a minimum tax-free annual living
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. Overview This project is part of the EPSRC Centre for Doctoral Training in Process Industries: Net Zero (PINZ) . The aim of the PINZ CDT is to train the next generation of chemical engineers and chemists
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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early
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Imperial College London Department of Mechanical Engineering PhD Studentship in Biomechanics: Improving the Performance of Total Knee Replacements Applications are invited for a research studentship
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critical component of this is the powder blending process, where active pharmaceutical ingredients (APIs) must be uniformly mixed with excipients to ensure precise dosage and product quality. The performance
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We invite applications for a PhD project focused on fundamental research into novel low-emission ammonia combustion/oxidation processes. This position is based within the Faculty of Engineering at
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Surface features are important in additive manufactured parts. While Additive Manufacturing technology has made great strides in the realisation of complex shapes and tailored topologies, it still
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Mission-Inspired priorities of Engineering Net Zero and Artificial Intelligence. This vision also reflects RCA’s institutional research priorities: Climate Crisis and the Circular Economy, and Design & AI
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This project is a collaborative research effort between
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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