Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
, 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
-
. 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
-
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
-
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
-
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
-
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
-
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
-
– [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
-
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
-
Robotics, Embedded Systems, Medical Sensors, Sensor Design