PhD studentship

PhD studentship in Improving Two-step Prediction Models in Consumer Research

Application deadline: 17 March 2017

We seek strong candidates for a 4-year, joint Master's and PhD research studentship in the EPSRC Centre for Doctoral Training (CDT) in Data Science at the University of Edinburgh, on the topic of “Improving Two-step Prediction Models in Consumer Research”.  This studentship is co-funded by Unilever and will be jointly supervised between the School of Mathematics and Unilever.

The research project will focus on developing new statistical models and techniques for predicting consumer preferences by combining different types of data. The project will focus on improving the standard approaches, incorporating different sources of error and subsequent propagation of these errors through the different testing processes.

Candidates should hold a good honours degree in Mathematics/Statistics or related subject, including a significant statistics component.

Studentship suitable for EU/UK students only.

About the School of Mathematics at University of Edinburgh

The School of Mathematics, and in particular the Statistics group, has undergone a period of rapid expansion in recent years, reflecting the growth of Data Science and the associated key foundational tools needed for analysing data. The Statistics group within the School has particular strength in applied Bayesian methodology and is currently led by Prof Ruth King, the Thomas Bayes’ Chair of Statistics.


About Unilever

Unilever is one of the world's largest and most successful fast-moving consumer goods companies. Keeping its products in great shape is critically important, which is why its business is underpinned by a dynamic and innovative research base, both in the UK and worldwide.


About the ESPRC CDT in Data Science

The CDT focuses on the computational principles, methods, and systems for extracting knowledge from data. Large data sets are now generated by almost every activity in science, society, and commerce - ranging from molecular biology to social media, from sustainable energy to health care. Data science asks: How can we efficiently find patterns in these vast streams of data? Many research areas have tackled parts of this problem:

  • the mathematical fields of statistics and optimization provide foundational tools and theory;
  • machine learning focuses on finding patterns and making predictions from data;
  • databases are needed for efficiently accessing data and ensuring its quality;
  • ideas from algorithms are required to build systems that scale to big data streams;
  • natural language processing, computer vision, and speech processing consider the analysis of different types of unstructured data.

Recently, these distinct disciplines have begun to converge into a single field called data science.

The CDT is a 4-year programme: the first year provides Masters level training in the core areas of data science, along with a significant project. In years 2-4 students will carry out PhD research in Data Science, guided by PhD supervisors from within the Centre. The CDT is funded by EPSRC and the University of Edinburgh.

Edinburgh has a large, world-class research community in data science to support the work of the CDT student cohort.  The city of Edinburgh has often been voted the 'best place to live in Britain', and has many exciting cultural and student activities.

Apply now

Applications must be received by 17 March 2016. Further enquiries can be made to Prof Ruth King. Please apply through the CDT in Data Science application page.