Job: Research Associate in Bayesian statistics at Imperial College London

Applications are invited for a Research Associate position in the Department of Mathematics at Imperial College London to work in the area of Bayesian Non-Parametric statistics. The position is funded through the EPSRC Grant EP/R013519/1. The Research Associate will work directly with Dr Sarah Filippi who holds a joint position between the Department of Mathematics and the School of Public Health. The advertised position is based in the vibrant Statistics section of the Department of Mathematics, and is to work in collaboration with researchers in the School of Public Health and at the MRC-PHE Centre for Environment and Health.

The post holder will work on developing a novel Bayesian Non-Parametric Test for Conditional Independence. This is at the core of modern causal discovery, itself of paramount importance throughout the sciences and in Machine Learning. As part of this project, the post holder will derive a Bayesian non-parametric testing procedure for conditional independence, scalable to high-dimensional conditioning variable. To ensure maximum impact and allow experimenters in different fields to easily apply this new methodology, the post holder will then create an open-source software package available on the R statistical programming platform. Doing so, the post holder will investigate applying this approach to real-world data from our established partners who have a track record of informing national and international bodies such as Public Health England and the World Health Organisation.

This should position the post holder ideally for the next steps in their career, by furthering their track record of bridging theory and applications in concrete ways.

The successful candidate must hold a PhD, or equivalent level of professional qualifications in statistics, mathematics, computer science or closely related discipline.

It is essential that you have:

Experience and knowledge:

Desire to develop statistical methodology for conditional independence testing in a Bayesian Non-Parametric framework. Experience in carrying out research of high quality, independently and/or in a team, evidenced by publications of high quality.
A strong background in statistics .

Skills and abilities:
Ability to work and communicate effectively in a multi-disciplinary team
Ability to carry out original research and publish in high impact journals
Ability to exercise initiative and judgment in carrying out research tasks
Ability to prioritise own work in response to deadlines
Ability to identify, develop and apply new concepts, techniques and methods
Creative approach to problem-solving
Ability to organise and prioritise own work with minimal supervision
Ability to keep accurate records of research results and activity
Excellent written communication skills and the ability to write scientifically, clearly and succinctly for publication
Ability to present research with authority and coherence

Please complete and upload an application form as directed, also providing a CV and a list of publications.

For any specific queries regarding the post please contact Dr Sarah Filippi (s.filippi@imperial.ac.uk)

Should you have any queries about the application process please contact Ms Mona El-Khatib, (m.el-khatib@imperial.ac.uk ).

For technical issues when applying online please email recruitment@imperial.ac.uk

Closing Date: 25th May 2018