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. This interdisciplinary chemical biology and plant biology project will involve training in cutting-edge science including computational design and peptide/small molecule synthesis, as well as evaluation of the compounds
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challenge. This project aims to increase the technology readiness level of microwave NDE by addressing fundamental laboratory challenges and transferring techniques to practical use. The project involves
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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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to: https://www.imperial.ac.uk/mechanical-engineering/research/ For information on how to apply, go to: http://www.imperial.ac.uk/mechanical-engineering/study/phd/how-to-apply/ For further details
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each category). Acceptable first degree: Geophysics, Geology, Computer science, Physics, Environmental Science Mode of Study Full-time Start Date 1 October 2026 Funding Information ARIES studentships
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(e.g., academia, pharmaceuticals/materials industry, data science). Additionally, you will gain research and communication skills, including a strong emphasis on integrating computational and
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the programme code: 8040F · select ‘PhD Civil Engineering (Structural) (full time) – 8040F as the programme of study You will then need to provide the following information in the ‘Further Details’ section
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observations and modelling of the physics and biogeochemistry of Antarctic shelf seas. You will gain experience in computer coding, statistics for environmental science, working with and piloting autonomous
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Data-driven predictions of dynamical systems are used in many applications, ranging from the design of products and materials to weather and climate predictions. Mathematical concepts from geometry
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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