Multi-omics and single-cell omics approaches are transforming our understanding of stem cell-based regeneration of tissues and organs on the individual cell level. By generating and leveraging the high-throughput and large-scale omic data generated in our lab as well as those that are publicly available, we aim to develop machine learning and statistical modelling methods to characterise the identities of various stem cells and subsequently predict their fate and lineages.
In this project, we will be working on developing state-of-the-art machine learning algorithms that could integrate “trans-omics” data that cut across multiple regulatory layers (cell signalling, transcriptional, translational, and epigenomic layers) for reconstructing trans-regulatory networks. We will next identify the key nodes of these networks for finding key determinants that define cell identities and cell fate decisions. The knowledge and the predictive power acquired from your work will be transformative for next-generation machine learning-based precision stem cell therapy.
Associated Scholarship is available for this project, please see it here (https://www.cmri.org.au/Research/For-Students/CMRI-PhD-research-award).
HDR Inherent Requirements
In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:
The opportunity ID for this research opportunity is 2685