We will have an opening for a two-year full-time Postdoctoral Research Assistant, in the areas of kernel methods, Gaussian processes, or probabilistic programming. Queries should be addressed to Professor Yee Whye Teh (firstname.lastname@example.org). The opening is funed by the ERC project “BigBayes: Rich, Structured and Efficient Learning of Big Bayesian Models”. We are generally interested in developing methodology, theory and applications of non-parametric models, which are highly flexible models used to directly parameterise and learn about functions, densities, conditional distributions etc.
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.