PhD opportunity: Intelligent selection and recycling of features in data analysis problems, University of Bath (4 year studentship with enhanced stipend)
Modern organisations produce large amounts of data, which need to be monitored to ensure the stability and efficiency of the organisation. Examples may include fault monitoring, customer interactions, or systems stability. In many cases, statistical modelling of the time series data may require significant expertise to analyse and careful observation to detect changes in the structural relationships.
The aim of this project is to develop novel statistical methodologies which can automate much of this complex modelling process to enable improved data analysis across many areas of the organisation. The project is in collaboration with BT, who will provide expertise and examples of real-world challenges.
This project will look to combine the use of dynamic time series models and appropriate Bayesian inference methodology, for example sequential Monte Carlo/particle filtering. These inference methods are particularly suitable to dynamic models with complex structures and intractable or computationally infeasible calculations, where traditional likelihood-based techniques have difficulties.
Further details can be found here:
Intelligent selection and recycling of features in data analysis problems at University of Bath on FindAPhD.com
