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Intelligent selection and recycling of features in data analysis problems

   Department of Mathematical Sciences

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  Dr Matthew Nunes, Dr Alexander Cox  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

The University of Bath is inviting applications for the following funded PhD project commencing in October 2021.

Funding is available to candidates who qualify for Home fee status. Following the UK’s departure from the European Union, the rules governing fee status have changed and, therefore, candidates from the EU/EEA are advised to check their eligibility before applying. Please see the Funding Eligibility section below for more information.

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, the underlying data can be complex, and 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 and provide easy-to-use diagnostic software to enable sophisticated 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.

Departments and research divisions within BT collect data over long periods of time, measuring many indicators of business performance such as fault intake, customer order volume or network stability. The dynamics and interactions between such indicators are dependent on various external influences and display seasonalities and complex nonlinear relationships. Deciding which predictors (features) to use in statistical models plays an integral role in predictive and classification ability. The number of such predictors considered for modelling BT data can be in the hundreds, and often it is natural to choose candidates within groups of predictors and within those specified by transformations of features. Up until recently practitioners within BT have fitted such models by hand, which is time-consuming and costly, and thus automating such approaches is vital. Moreover, the end-users of the models are often not experts, and need models to be easily interpreted. A second strand of this project concerns the development of interpretable approximate models for use by non-experts.

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.

NOTE: Applications may close earlier than the advertised deadline if a suitable candidate is found; therefore early application is recommended. 

Candidate requirements:

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent) in Mathematics, Statistics or another relevant discipline. A master’s level qualification in Mathematics or Statistics would be beneficial.

Non-UK applicants will also be required to have met the English language entry requirements of the University of Bath.

Enquiries and applications:

Informal enquiries are welcomed and should be directed to Dr Matthew Nunes ([Email Address Removed]).

Formal applications should be made via the University of Bath’s online application form for a PhD in Statistics (full-time).

See our website for more information about applying for a PhD at Bath.

Funding Eligibility:

In order to be considered for a studentship, you must qualify as a Home student. In determining Home student status, we follow the UK government’s fee regulations and guidance which are available to view on the website of the UK Council for International Student Affairs (UKCISA). As a guide, the main categories of students generally eligible for Home fee status for 2021/22 entry are:

  • UK nationals (who have lived in the UK, EU, EEA or Switzerland continuously since September 2018)
  • Irish nationals (who have lived in the UK or Ireland continuously since September 2018)
  • EU/EEA applicants with pre-settled status or settled status in the UK under the EU Settlement Scheme (who have lived in the UK, EU, EEA, Switzerland or Gibraltar continuously since September 2018)
  • Applicants with indefinite leave to enter/remain in the UK (who have been resident in the UK continuously since September 2018)

EU/EEA citizens who live outside the UK are unlikely to be eligible for Home fees/funding.

Additional information may be found on our fee status guidance webpage and on the GOV.UK website.

Funding Notes

Candidates applying for this project may be considered for a 4-year EPSRC Industrial CASE studentship with BT. Funding covers tuition fees, an enhanced stipend (2021/22 UKRI rate £15,609 per annum + £3,000 per annum top-up from the industrial partner, subject to contract) and a generous budget for research expenses, training and conference attendance. Eligibility criteria apply – see Funding Eligibility section above.


Lowther, A., Fearnhead, P., Nunes, M. A. and Jensen, K. (2020) Semi-automated simultaneous predictor selection for Regression-SARIMA models. Statistics and Computing, 30, 1759-1778.

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