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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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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
nature makes them susceptible to complex damage modes like delamination, fibre breakage, and matrix cracking, especially under high-velocity impacts from projectiles or debris. Current assessment
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(CHF) phenomena – the prediction of which is key to safely designing and operating water based nuclear reactors. Current industrial modelling tools necessitate excessively conservative safety margins
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undergraduate degree relevant to each individual project, e.g., Experimental Psychology, Physics, Neuroscience, Computer Science, Electrical Engineering, or a related subject. In addition, an appropriate master’s
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Location: Central Cambridge A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on non-equilibrium turbulent flows over rough walls. The post holder will
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Eligibility criteria You must have, at least a 2:1 honours degree or international equivalent, in a subject relating to neuroscience or neural engineering. Further qualifications such as an MRes is advantageous
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families of quantum codes such as permutation invariant codes and some bosonic codes. This PhD will explore the theoretical application and optimisation of these quantum codes for quantum technologies
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partnership with the Royce Institute and Cummins, you will study the microstructure of nickel-based alloys used in turbine wheels — vital components for hydrogen-ready engines and future power technologies
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alternatives. Yet, large-scale commercialisation of bioplastics faces environmental and economic challenges. Current feedstocks pose trade-offs: edible crops such as maize and sugarcane raise food security
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to create molten salt MLIPs, predicting a variety of industrially pertinent properties: thermal conductivity, heat capacity, viscosity, and thermodynamic phase data. We will develop new statistical mechanical