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SCDTP Studentship - Big Data Analytics and Machine Learning in the Service Sector

Southampton Business School

Location:  Highfield Campus
Salary:   £14777
(18/19 UKRI rate)
Closing Date:  Thursday 24 January 2019
Reference:  1080418TR

Emotion regulation, reward-related neural activity, and well-being

A fully funded studentship awarded by the Economic and Social Research Council (ESRC) South Coast Doctoral Training Partnership (SCDTP) commencing in 2019/20 Academic Year.

Supervisory Team:

Dr Tahir Nisar (, Professor Amy Fraher, Professor Frank McGroarty 

The research project will optimize online data utilization techniques such as how to handle the huge volume of consumer journey data in the insurance and financial services lifecycle more efficiently. Combined with the knowledge of the business and user behaviour, the project will identify valuable touch points and shed light on how to allocate the budgetary resources as per business needs and preferences. The project will first describe the data sets and identify their value; and then develop a quantitative modelling framework, including machine learning techniques such as basket market analysis, association rules mining, clustering, and sequential pattern analysis.

Skills Required

Basic knowledge of statistics and quantitative modelling techniques


South Coast DTP Funding provides an annual maintenance grant (tax free) of £14777 (2018/19 UKRI rate), plus payment of all programme fees.  Other funding available for SCDTP funded students can be found on the SCDTP website (

Funding is provided for 3 years full-time PhD study (pro-rata for part-time students).  Applications for 1+3 funding for students completing a Master's year prior to the commencement of PhD study are also welcome (details available at

Application Procedure

The closing date and time for applications is 11.59pm on 24th January 2019.  The full application procedure, the funding application form, and more information on the South Coast Doctoral Training Partnership can be found at:

For further information about this project, please contact the lead supervisor detailed above.  For questions relating to the application procedure, or for more information about the SCDTP, please visit the SCDTP website or contact us at

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