About the project
Gene Regulatory Networks (GRNs) orchestrate cell fate and are key to understanding disease. We has expertise in developing computational methods for single-cell omics data. This project will develop new computational approaches for single-cell RNA-seq, applying them to human datasets to discover targets and advance precision medicine.
Single-cell RNA sequencing (scRNA-seq) provides an unprecedented view of cellular function, generating complex, high-dimensional data that requires sophisticated computational approaches for interpretation. This project focuses on developing and applying machine learning (ML) methods to scRNA-seq data derived from human normal and disease datasets. The project may involves developing and applying ML approaches towards identifying key regulators and networks from noisy, high-dimensional single-cell data. This computational modelling may lead to the formulation of precise, testable hypotheses about key transcriptional regulators in disease.
As the project advances, you would apply these robust computational framework to analyze large-scale scRNA-seq datasets cohorts from diverse human normal and disease systems (e.g., development, cancer, immunity). The project would involve implementing and benchmarking these novel ML methods into a robust pipeline to accurately map cell-state transitions and regulatory interactions.
Through this PhD, you will master highly sophisticated computational biology and analytical techniques, including single-cell data processing and advanced model development, skills which are in exceptionally high demand across industry and academia. This interdisciplinary training, will benefit from collaborators, and will equip you with interdisciplinary skills.
You will receive hands-on training in single-cell data analysis, machine learning, biostatistics, high-performance computing (HPC), and software development/best practices.