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of the training data. Hardware vendors have begun to design specialised hardware accelerators that can perform very efficiently a limited range of operations using low-precision formats such as FP8, binary16
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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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This PhD project aims to advance Safe and Sustainable by Design (SSbD) pharmaceutical manufacturing by integrating cutting-edge methodologies, including computer-assisted retrosynthesis, end-to-end
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necessary to use all epidemiological tools, including microbial genomics, to support this endeavour. This exciting project seeks to using whole genome sequencing measles data to advance the understanding
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-mounted ground-penetrating radar (GPR) with smart meter data to transform leak detection. Drones equipped with GPR will identify underground anomalies, while smart meter data will refine localisation
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materials. The computer modelling of LSP remains challenging due to its multi-physics and multi-scale nature. The dependency of the process on the shape of the laser pulse, its energy, ablation layers etc. is
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distinct imaging methods to yield a novel imaging method that combines the benefits of both. The aim of this project will be to develop a novel method for fusing the data obtained by x-ray imaging and MIS
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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in leaf size, shape and surface texture, all species have an astonishing ability to self-right when flipped over. Preliminary data indicate that drastic differences in surface wettability between