Applications are invited for two full-time ERC-funded Postdoctoral Research Assistants to work on the project ‘BigBayes: Rich, Structured and Efficient Learning of Big Bayesian Models’ in the Department of Statistics. The postholders will be responsible for conducting world class research into the methodology, theory and applications of non-parametric models. Non-parametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc, and have been successfully applied to regression, survival analysis, language modelling, time series analysis, and visual scene analysis among others. They are increasingly popular in machine learning, statistics and other data analytic fields.

You will be expected to work on cutting edge methodological research, apply developed methodologies to problems in a variety of domains, collaborate with colleagues in external institutions and research groups, and present papers at conferences and workshops. You will act as a source of information to other members of the group, communicating effectively both in person and on paper. You will manage your own academic research and administrative activities.

Candidates should hold a PhD/DPhil in machine learning, statistics, computer science or affiliated discipline and have significant relevant experience in non-parametrics, probabilistic modelling, Bayesian methodologies, kernel methods, Monte Carlo methods, computational statistics, or large scale probabilistic inference.

The posts will be supervised by Professor Yee Whye Teh, and are fixed-term for 2 years. To apply for this role and for further details, including the job description and selection criteria, please click on the link here.

Queries about these posts should be addressed to Professor Yee Whye Teh ( or Dr Sarah Filippi (

The closing date for applications is 12.00 noon on 8 August 2016. Interviews will be held on 9 September 2016.