PhD Studentship in Statistical Science

PhD Studentship in Statistical Science

The Project: The School of Mathematics at the University of Bristol invites applications for a PhD Studentship position in Statistical Science.

The aim of the project is to work on scaling up Bayesian computational methods in order to address current challenges posed in various areas of science and engineering. The project involves the use of established and recently proposed computational methods in combination with ideas from machine learning in order to make the use of the Bayesian paradigm routine for currently challenging scenarios. The project can be tailored to the applicant’s profile to explore practical, methodological or theoretical aspects involved in this research programme.

The project is linked to the “Bayes4Health: New Approaches for Bayesian Data Science: Tackling Challenges from the Health Sciences” ( and “CoSInES:  Computational Statistical Inference for Engineering and Security” (  research programmes, and the PhD student will be expected to interact with the two Bristol based research associates involved in these projects as well as other researchers. 

The supervisors for this project are Prof. Christophe Andrieu (School of Mathematics, Bristol) and Prof. Mark Beaumont (Life Science, Bristol).

Funding:  Includes tuition fees, research travel grant and a full stipend at the EPSRC DTA rate (£14,777 in 2018/19) for 3.5 years.

Further information

How to apply: Please make an online application for this project selecting Mathematics (PhD) as the programme choice. When prompted in the Funding and Research Details sections of the form, specify that you wish to be considered  for the Christophe Andrieu DTA studentship.

Additional advice on how to complete your application can be found on our postgraduate application advice page.

Candidate requirements:  To be considered for this funded PhD studentship, applicants must hold (or expect to receive) a First Class degree (or equivalent) in MathematicsKnowledge of probability and statistics, particularly point processes, graphs, and time series, as well as programming experience are desirable.

Further details: Prof. Christophe Andrieu