Survival Analysis using temporal patterns


Contact Dr Georgios Aivaliotis and Dr Jan Palczewski to discuss this project further informally.

Project description

Temporal pattern mining is an approach of representing complex and unstructured temporal data (for example health data, credit event data end more) through temporal patterns for inference using statistical analysis or machine learning methods. Survival time is in general defined as the time to a particular event. The application of survival methods to data recoded through temporal patterns poses challenging research questions as to how survival should be defined and classical statistical and machine learning methods should be adapted to accommodate temporal patterns.

Entry requirements

Applications are invited from candidates with or expecting a minimum of a UK upper second class honours degree (2:1), and/or a Master's degree in a relevant mathematics degree such as (but not limited to) statistics, finance and data analytics.

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 the 'Survival Analysis using temporal patterns’ as well as Dr Georgios Aivaliotis as your proposed supervisor.

If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.

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.