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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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. 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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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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Engineering at the University of Leeds (UoL) and by Diamond Light Source (DLS). The successful student will spend 50% of their time at the UoL and the other 50% across crystallography beamlines I11 (High
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year for 3.5 years is also available to previous graduates of the University of Leeds. Lead Supervisor’s full name & email address Dr William Collinge – [email protected] Co-supervisor name(s) Dr
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systems to meet stringent emission legislation and simultaneously reduce carbon emissions. The project will use a novel patented catalyst (for NOx and CO reduction) developed at the University of Leeds and
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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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– [email protected] Project summary Drug discovery pipelines are driven by iterative cycles in which molecules are designed, synthesised, purified and tested. A remarkably limited toolkit of reactions
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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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Robotics, Embedded Systems, Medical Sensors, Sensor Design