Research Associate in Bayesian Non-Parametric statistics at Imperial College London

The International Society for Bayesian Analysis; City of Westminster; On-site; GBP 30,000 - 40,000; Full time

Job summary

A prestigious educational institution in the UK seeks a Research Associate in Bayesian Non-Parametric statistics. The role involves developing methodologies and software for real-world applications. Ideal candidates should have a PhD and a background in statistics, along with strong communication and research skills. Collaborative work with public health data is crucial. This position presents an excellent opportunity to advance in a dynamic academic environment.

Qualifications

• Desire to develop statistical methodology for conditional independence testing.
• Experience in high quality research with publications.
• Strong background in statistics.

Responsibilities

• Develop a novel Bayesian Non-Parametric Test for Conditional Independence.
• Create an open-source software package on R platform.
• Apply methodology to real-world data from established partners.

Skills

Statistical methodology development

Effective communication in multi-disciplinary teams

Initiative and judgment in research tasks

Education

PhD in statistics, mathematics, computer science or closely related discipline

Job description

Research Associate in Bayesian Non-Parametric statistics at Imperial College London

Apr 24, 2018

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.

Responsibilities

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.

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 ([e-mail]).

Should you have any queries about the application process please contact Ms Mona El‑Khatib, ([e-mail]).

For technical issues when applying online please email [e-mail]