Research Associate in Bayesian Non Parametric at Imperial College London

The International Society for Bayesian Analysis; City Of London; On-site; GBP 35,000 - 45,000; Full time

Job summary

A leading educational institution in London is seeking a Research Associate in Bayesian Non-Parametric statistics. The successful candidate will develop a Bayesian Non-Parametric Test for Conditional Independence, collaborating with experts in the field. This role is crucial for advancing causal discovery in public health, epidemiology, and machine learning applications.

Qualifications

• Strong background in Bayesian Non-Parametric statistics.
• Experience in causal discovery methods.
• Familiarity with machine learning applications.

Responsibilities

• Develop a novel Bayesian Non-Parametric Test for Conditional Independence.
• Collaborate with Dr Sarah Filippi and Prof Chris Holmes.
• Contribute to causal discovery in various scientific fields.

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.