497 structural-engineering-"https:"-"https:"-"https:" PhD positions in United Kingdom
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library of polymer candidates with varied physicochemical properties (charge, size, structure, biodegradability) Formulate polymer–mRNA nanoparticles and characterise their size, stability, and mucus
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group with structured training and strong technical support. 2. Candidate profile This project is ideal for candidates from Mechanical, Electrical, Biomedical, Materials Engineering, or related
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, materials science, environmental engineering, or a related field - Strong laboratory skills and an interest in sustainable construction technologies - Experience with cementitious materials, durability
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modifiers to mitigate this problem, but many of the existing additions have been discovered by chance. To allow more control of structure during the solidification stage as the amount of recycled content is
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climate change-resilient infrastructure slopes. This PhD project will investigate the impact of different intervention types (e.g. structural measures, modified materials, vegetation, drainage, and/or cover
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embrittlement, leakage, and long-term material degradation raise critical reliability concerns for flight certification. Structural Health Monitoring (SHM) and Digital Twinning (DT) technologies can address the
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treatment outcomes in vulnerable populations. Training and Student Development: The student will receive interdisciplinary training in: Peptide synthesis and structural analysis Advanced mass spectrometry
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The emergence of 3D concrete printing (3DCP) is reshaping the future of construction, offering unprecedented design freedom, material efficiency, and automation. Unlike conventional concrete casting
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exotic crystalline structures that remain poorly understood despite extensive experimental investigations. This project combines cutting-edge sampling techniques with developing machine-learned potentials
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temporal structure and missing data affect multimodal learning Identifying fundamental trade-offs between accuracy, efficiency, and robustness. The project emphasises foundational machine learning research