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. Being able to predictively design particle properties is of great economic value and is applicable to a range of industries such as pharmaceuticals, agrochemical, additives, cosmetics and food
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Robotics, Embedded Systems, Medical Sensors, Sensor Design
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-molecular interactions will provide a wealth of new inforamtion, which can be used to improve predictive design and control of crystallisation using machine-learning methods. This studentship will entail
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, this project with apply the result to further train neural network-based machine learning models, with the aim of developing predictive tools for the rational design of future materials. Please state your entry
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patient-specific implant designs. You will aim to develop a combination of in silico and in vitro models of wrist repair that can assess the risk of tissue damage following surgery. The PhD project will
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of photonic qubits is the capability to generate large numbers of single photons with identical quantum properties. Increasing the level of indistinguishability of single photons optically requires designing
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to minutes per ligands. The most successful ligands identified in silico will be prepared and validated experimentally. Further refinement of the lead ligands through rational design, computational evaluation
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relates to its crystal packing. Designing a spin-crossover material with bespoke properties for a specific application is a challenging problem, which hinges on the structure, dynamics and cohesive forces
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or without hysteresis, is controlled by the crystal packing in the bulk material rather than the molecule itself. We aim to understand the relationship between structure and function, so we can design new spin
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predictive design of functional materials and hence their manufacturing. There will also be opportunity to carry out electron diffraction and X-ray spectroscopic analysis on these systems. Entry requirements