Data collected routinely for different purposes carry a wealth of information with the potential of significant impact to improving people’s life (e.g. personalised medicine, smart cities, screening for diseases, pollution, lifestyle and health and more).
That potential increases further when different data sources are jointly analysed, however, the inherent heterogeneity of the data and the temporal and non-linear relationships between variables, makes application of statistical methods a challenging problem. While machine learning methods can deal effectively with the non-linear relationships and the high dimensionality of the problem, they lack the interpretability of statistical models. However the interplay between statistics and machine learning can offer significant advances to the field.
This research project will aim at exploring the interplay between machine learning and statistics in establishing new methods to model such complex systems.
Applicants should have, or expect to obtain, a minimum of a UK upper second class honours degree in Mathematics, Statistics or a related discipline, or equivalent. Applicants whose first language is not English must also meet the University's English language requirements.
If English is not your first language, you must provide evidence that you meet the University's minimum English Language requirements.
How to apply
Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is 'Temporal models for heterogeneous datasets' as well as Dr Georgios Aivaliotis as your proposed supervisor.
We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.
If you require any further information please contact the Graduate School Office, e: email@example.com