Research Associate in Bayesian Non Parametric at Imperial College London

The International Society for Bayesian Analysis; London; On-site; GBP 30,000 - 45,000; Full time

Job summary

A leading research organization in London is looking for a Research Associate to contribute to Bayesian Non-Parametric statistics. The candidate will develop innovative tests for causal discovery, essential in various scientific fields, collaborating with prominent researchers in mathematics and machine learning. This position presents an excellent opportunity for those passionate about advancing statistical methodologies in real-world applications.

Qualifications

• Experience in Bayesian Non-Parametric statistics.
• Ability to develop novel statistical tests.
• Familiarity with causal discovery methods.

Responsibilities

• Develop a Bayesian Non-Parametric Test for Conditional Independence.
• Collaborate with research teams on causal discovery.

Job description

Research Associate in Bayesian Non Parametric at Imperial College London

Jul 20, 2018

Research Associate in Statistics

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 and in collaboration with Prof Chris Holmes (Alan Turing Institute).

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. In particular, the discovery of causal relationships is fundamental in public health and epidemiology, in life and earth sciences, in sociological studies and in econometrics.