Dr J Shi, Prof L Rochester
No more applications being accepted
Competition Funded PhD Project (Students Worldwide)
About the Project
One of difficult problems in functional data analysis is to deal with data with different types and with big noise. This project is focused on the wavelet basis transformation, using either the discrete or continuous wavelet transformation (DWT and CWT). Basically, DWT is a basis transform for functional data (or the original process data). The transformed data, in the form of the wavelet coefficients, characterize functional features of different scales at different locations. The analysis of the original functional data is therefore converted into the analysis of the WDT location-scale wavelet coefficients in functional data analysis framework. A new Bayesian classification model will be developed using spike-and-slab prior on the location-scale tree (namely Markov groves) and will be applied to analyse gait data for precision medicine in dementia or Parkinson diseases.
It should be noted, that successful applicants will also be given the opportunity to complete teaching and demonstrating duties within the school amounting to up to £1500 per annum.
Funding Notes
This studentships is available to UK/EU and International candidates, who have/expect a 2:1 honours degree in computing science, mathematics, physics, statistics or another strongly quantitative discipline, or an international equivalent.
Applicants whose first language is not English require a minimum of IELTS 6.5. International applicants may require an ATAS (Academic Technology Approval Scheme) clearance certificate prior to obtaining their visa and to study on this programme.
The studentship includes tuition fees, a tax-free stipend of (up to) £14,296pa (16/17 level), a desktop computer, and £1500 travel allowance.
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