Recent advanced and increased sophistication in collecting single-cell RNA seq data has led to a need for quantitative tools to understand and interpret this data. We approach this problem from a theoretical perspective. Recent work has identified that studying the cell-cell correlations from single-cell data can allow the identification of cell-state transitions. However, this identification is likely obscured by noise, lineage and other sources of correlations. The project will involve simulating the dynamics of artificial gene-networks and identifying different sources of correlations between cells when they are going through state transitions (e.g. during differentiation).
1. Post Name: Graduate Trainee
2. No. of Post: 01
3. Essential Qualification and Experience required: Bachelor or Master's in physics, math, engineering or computer science.
4. Skills required: Some programming experience in any computer programming language of your choice (e.g. C++/Python/Fortran etc.). Comfortable with calculus, linear algebra and statistics Some basic knowledge of cell biology is preferred, but not required
5. References: Freedman, Simon L., et al. "Revealing cell-fate bifurcations from transcriptomic trajectories of hematopoiesis." bioRxiv (2021). Nitzan, Mor, and Michael P. Brenner. "Revealing lineage-related signals in single-cell gene expression using random matrix theory." Proceedings of the National Academy of Sciences 118.11 (2021). Qin, Chongli, and Lucy J. Colwell. "Power law tails in phylogenetic systems." Proceedings of the National Academy of Sciences 115.4 (2018): 690-695.
6. Minimum % of marks: 75%
7. Salary offered: 25,000 (without CSIR/GATE/equivalent qualification),31000 + 24% HRA (with CSIR/GATE/equivalent qualification)