Job description

Job summary

We are seeking a Research Associate for Bayesian Statistics and Causal Inference with a strong background in Bayesian and computational statistics or machine learning and causal inference, including causal structure learning, to develop novel causal inference tools tailored for single-cell sequencing data, though knowledge of molecular biology or neuroscience is not required. Single-cell RNA sequencing measures the transcriptome at single-cell resolution and provides a detailed snapshot of gene-regulation in individual cell-types in contrast to standard bulk measurements where expression is averaged over all cell-types.

This project is based on the world-largest single-cell RNA-sequencing dataset of the human brain derived from 141 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 first expected outcome 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.

The second outcome is to define novel drug targets for a wide spectrum of human diseases, in particular brain diseases, expected to accelerate the translation of target discovery to therapeutical insight. 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.

Duties and responsibilities

• To develop novel Bayesian inference methodology and models for structure causal inference

• To develop machine learning solutions for structure causal learning

• To implement the designed models as efficient and user-friendly software (e.g., R, Python, PyTorch) and maintain software under GNU general public license

• To analyse single-cell RNA-sequencing and genomics data

• To conduct open and reproducible research at the highest scientific standards

• To ensure the validity and reliability of data at all times

• Any other duties which may arise commensurate with the grade of the post as directed by Dr Zuber

• Any other duties as may be deemed reasonable by Head of group as well as Head of Division/Department/Section

Essential requirements

• Research Associate: Hold a PhD Bayesian statistics, computational statistics, machine learning, causal inference, and data science (or a relevant subject)

• Research Assistant: Near completion of a PhD Bayesian statistics, computational statistics, machine learning, causal inference, and data science (or a relevant subject)

• Postholder has made contributions to scientific papers

• Postholder has presented work at conferences

• Ability to steer (or contribute to steering) the direction of projects

• Knowledge of machine learning algorithms

• Detailed knowledge of causal inference

• Excellent coding skills, preferably in R, Python or PyTorch

• Low-level programming skills (e.g C++)

Further information

This is a full time, fixed term post for 2 years and you will be based at St Mary's Campus.

Should you require any further details on the role please contact: Dr. Verena Zuber (v.zuber@imperial.ac.uk ).

Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £38,194 - £41,388 per annum.

 NB: All interviews will be held a week after the closing date of the application deadline.

Documents

• Job-Description-Research-Associate-08.22-BMBR - DA.docx