Research Associate in Bayesian Statistics and Causal Inference, Imperial College London and Cam[...]

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

Job summary

A leading institution is seeking a Research Associate in Bayesian Statistics and Causal Inference to develop causal inference tools for single-cell sequencing data. The role involves collaborative research to advance the understanding of neurological diseases using cutting-edge methodologies and data sets.

Benefits

Generous travel and computing budget

Funding available for 3 years

Qualifications

• Strong background in Bayesian and computational statistics.
• Experience with machine and statistical causal structure learning.
• Knowledge of single-cell sequencing data.

Responsibilities

• Develop novel causal inference tools for single-cell sequencing data.
• Implement causal inference and structure learning methodologies.
• Collaborate with national and international experts.

Skills

Bayesian Statistics

Computational Statistics

Machine Learning

Causal Inference

Education

PhD in Statistics or related field

Job description

Research Associate in Bayesian Statistics and Causal Inference, Imperial College London and Cambridge, UK

Feb 15, 2023

*Key dates*:
· Closing date 27 February 2023
· Interviews will be held a week after the closing date of the application deadline

We are seeking a Research Associate in Bayesian Statistics and Causal Inference with a strong background in Bayesian and computational statistics or machine learning and causal inference, including machine and statistical causal structure learning, to develop novel causal inference tools tailored for single-cell sequencing data.

This project is based on the world-largest single-cell RNA-sequencing dataset of the human brain derived from 147 samples combined with genotype information to define molecular causes for neurological disease. It will also expand and use other publicly available single-cell datasets combined with genotype data. The main aim of this project consists of novel causal inference and structure learning methodologies as well as their software implementation tailored to, but not limited, to scRNA-seq.

This is a collaborative project with national and international experts in their field including Prof Michael Johnson, Professor of Neurology and Genomic Medicine, Imperial College, Dr Leonardo Bottolo, Reader in Statistics for Biomedicine, University of Cambridge, and Prof Guido Consonni, Professor of Statistics, Universita’ Cattolica del Sacro Cuore, Milan, Italy. The position is funded by the “MRC Better Methods, Better Research” panel and includes a generous travel and computing budget. The funds for this post are available initially for 3 years in the first instance.

Informal enquiries may be made to Dr Verena Zuber at [e-mail] or Dr Leonardo Bottolo at [e-mail]