Computational and statistical models for single-cell multi-omics data in immunology

Back to search results
PROJECT DESCRIPTION

Genomics and single cell technologies are revolutionising modern biomedical research, including understanding how immune systems protect the body from pathogens and cancer. These technologies generate very large and complex data sets comprising gene, protein and function information at the single cell level. However, an inability to adequately analyse these data is currently limiting the progress in this field. This project will develop cutting-edge statistical and computational models to learn the molecular pathways driving immune cells to a successful immune response against pathogens and cancer. This knowledge will inform novel immunotherapies for cancer and vaccines against significant pathogens affecting marginalised communities.

IDEAL CANDIDATE

The success of this proposal is very much dependent on an interdisciplinary team, including ambitious and talented students with a background in quantitative disciplines (e.g. Mathematics, Statistics, Bioinformatics, Theoretical Biology). The quantitative skills required should include computational statistics and statistical or machine learning, Bayesian inference techniques, stochastic simulation methods and very good coding skills in R and other bioinformatics-oriented languages such as Python, C, Matlab or Julia. Experience in biological problem solving is very helpful but not necessary. Candidates with a mathematical/statistical background should demonstrate a strong interest in learning the basis of immunology and genomics. The candidate should have outstanding communication skills, as this project will rely on interactions with multiple researchers across different fields of research. These are key position requirements for the successful development of novel computational and statistical models to understand very large and complex multi-omics data sets. The candidate should genuinely enjoy problem solving using quantitative methods and be willing to learn and work across disciplines. The supporting research group will offer a unique opportunity to work closely with successful experimental, clinical and quantitative researchers, as well as provide close interactions with a larger team of PhD students working on related topics.
Supervisory team
Pierre
Lafaye de Micheaux

Science
Mathematics & Statistics
Fabio
Luciani

Medicine
The Kirby Institute
Scott
Sisson

Science
Mathematics & Statistics
lafaye@unsw.edu.au