Postdoctoral Research Assistant in Statistical Machine Learning

University of Oxford - Department of Statistics

Applications are invited for a full-time postdoctoral research assistant in statistical machine learning, fixed-term for up to 2 years. Reporting to Professors Yee Whye Teh and Dino Sejdinovic, the postholder will be a member of the OxCSML (Oxford Computational Statistics and Machine Learning) research group with responsibility for carrying out research on the Oxford - Tencent AI collaborative project on Large-Scale Machine Learning. The funds supporting this research project are provided by Tencent AI until October 2020.

The postholder will be responsible for conducting world-class research into the methodology, theory and applications of deep generative models and hyperparameter optimisation. The postholder will also provide guidance to junior members of the research group including research assistants, PhD students, and/or project volunteers.

Applicants are sought who already have, or are close to the completion of, a doctorate in Machine Learning, Statistics, Computer Science or affiliated discipline, or who have equivalent experience.

The successful candidate will have significant relevant experience in several of the following research topics: unsupervised learning, generative models, deep learning, nonparametric modelling, kernel methods, Bayesian optimization, variational methods. They will also possess excellent and relevant specialist knowledge and expertise.

For further information about the position or the project, please contact Professor Yee Whye Teh ( or Professor Dino Sejdinovic ( Dr Jennifer Rogers ( can also be contacted for questions regarding the research environment at the Department.

The closing date for applications is 12.00 noon on Monday 8 January 2018. Interviews will be held on Friday 26 January 2018.

Please note that the University of Oxford's retirement policy has changed. With effect from 1 October 2017, all employees at Grade 8 and above have a retirement age of the 30 September before the 69th birthday. All employees at Grades 1-7 do not have a set retirement age. Further details are available here:

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