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to analyse complex datasets, extract meaningful insights, and guide the optimisation of drug molecules. Collaborate with internal groups, including the Centre for Additive Manufacturing (CfAM) to design and
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Research theme: Formal Methods How many positions: 1 This 3.5 year PhD is funded by the Department of Computer Science at The University of Manchester. The successful candidate will receive
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Abstract: Condition monitoring of industrial equipment, such as turbomachinery, is a complex task that requires accurate and efficient data collection but is often hindered by the equipment's size
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background in Computer Science, Mathematics. Students with interests in machine learning, deep learning, AI, uncertainty quantification, probabilistic methods are encouraged to apply. For eligible students
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(RNCM) and Olympias Music Foundation (OMF), which aims to address their shared aspiration to better understand the complex challenges of teaching music within diverse local communities. Situated within
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nature and industry are complex fluids, a heterogeneous mixture of at least two phases due to the presence of additives such as colloids, surfactants, and polymers. Examples include colloidal dye particles
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Funding: School of Computer Science studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake
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the geometrical approximation error. The increased fidelity of the modelling, however, comes at the cost of increased complexity in the shape function formulation, which in turns affects the numerical evaluation
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Computer Science Studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email address Dr. Ping Lu: p.lu@leeds.ac.uk