|Published||December 7, 2022|
|Location||Oxford, United Kingdom|
We invite applications for a postdoctoral research fellowship with interests in computational statistics and/or statistical machine learning to work on new tools for high-dimensional data analysis motivated by real-world studies in health and medical sciences. For instance, developing new techniques in causal inference for large observational data sets arising in medical and health related studies. The postdoctoral research fellow will have freedom to develop their own research around the central theme of optimization-based sampling supported by a world-leading group in modern statistical modelling.
As the successful applicant at grade 8, you will hold a PhD/DPhil in Computer Science, Statistics, Machine Learning, or an affiliated discipline or for grade 7 post, hold or be close to completion of a relevant PhD/DPhil. You will have outstanding coding skills in a language such as Python, R or C++ etc and have significant relevant experience in the development and study of inference and learning schemes. You will have the ability to conduct and complete high-quality research independently and be able to communicate results effectively, in person and on paper. Experience in one or more of the following areas: Causal Inference, Longitudinal data analysis, Statistical Machine Learning, Scalable Bayesian methods will be beneficial.
Queries about this post should be addressed to Professor Chris Holmes, email firstname.lastname@example.org
This post is fixed term for two years, in the first instance.
Only applications received before 12.00 midday on 4 January 2023 will be considered.
Interviews will be held on 27 January 2023.