Garvan Summer Scholarship Program 2025/26

THE OPPORTUNITY

The Summer Scholarship Program, provides an exceptional opportunity for currently enrolled undergraduate students to engage in research projects during the summer of 2025/2026.

This program is designed to immerse highly talented undergraduates, particularly those in Science or related disciplines, in the research process. It aims to enrich your educational journey and inspire a deeper interest in research or related fields.

Participants will gain invaluable experience by working alongside our esteemed supervisors on meaningful research projects, providing a fantastic insight into the research process and helping you determine if a research career is right for you.

The program spans 8 -10 weeks, offering 7 scholarship positions. Each scholarship is valued up to $5,000 - $6,250, with funding allocated at a rate of $625 per week based on the program's duration.

We encourage you to seize this opportunity to enhance your academic and professional development.

WHAT YOU WILL DO

Our range of projects for the Summer of 2025/2026:
1. Investigating fetal-like properties of malignant tumors using single-cell data and machine learning
Malignant tumors are capable of undergoing "oncofetal reprogramming" - a phenomena, which refers to cancerous tissues re-activating specific molecular programs similar to those of fetal development. This has been preliminarily shown to occur in liver and colorectal carcinomas, however, it needs to be further investigated in other tissues of origin and cancers, as well as studied with increased granularity. A wealth of fetal and cancer single-cell gene expression data has been generated over the last few years, which presents a path for a computational machine learning-based pattern search.
- What you will learn_ During this summer program, we are looking for a student who would be interested in using transcriptomic data to conduct computational biology research and to build a relevant statistical model that would further elucidate the fetal-like properties of cancer tissues.
- Prerequisites_ Basic coding skills and some molecular biology knowledge, or a desire to learn those

2. Functional annotation of prostate cancer risk loci using machine learning models of multi-omic data from prostate stromal cells
Large-scale genome wide association studies (GWAS) have identified hundreds of genetic loci associated with prostate cancer risk. However, the biological mechanisms by which these loci influence the development of prostate cancer is not fully understood. Prostate tumours arise from epithelial cells. However cancer thrives in a tumour microenvironment comprising multiple cell types including fibroblasts. Importantly, these prostate fibroblasts interact with epithelial cells to influence tumour progression.
We hypothesise a novel mechanism whereby prostate GWAS loci occurring in regulatory regions in the fibroblast cells, may act by modifying prostate fibroblast cell gene expression and associated phenotype, to influence prostate cancer tumorigenesis.

We have generated multi-omic datasets from normal and tumour prostate epithelial and fibroblast cells, including: ChIP-seq - histone modifications; HiC and PCHiC - 3D chromatin mapping; WGBS - DNA methylation; and RNAseq - gene expression.
- What you will learn_
- Prerequisites _R, statistics

3. Enhancing reproducibility and scalability in mass spec imaging data analysis through optimised workflow management.
Mass spectrometry imaging (MSI) is a state-of-the-art technique allowing spatial characterisation of molecules in tissues. We have developed an R-based analysis pipeline that enables researchers to interrogate MSI data to uncover novel biological insights in cancer.

In this project, the student will work on enhancing the utility of our pipeline through designing and implementing a comprehensive workflow management system. The existing code serves as the foundation, but lacks streamlined procedures, code modularisation, and error checking that are necessary for managing, reproducing and scaling complex analyses, crucial in the context of MSI data due to its intricate and voluminous nature.

The project will be cross-supervised by researchers from the Cancer Ecosystems program and the Data Science Platform
- Prerequisites _Familiar with R and command line. Familiarity with git and containers are desirable but not required.

4. Visualisation of logic-derived annotation space for structural variants implicated in genetic disorders
- What you will learn_
The student will learn about best practices for data processing in genomics; well-established genomic tools; methods for detecting and analysing structural variants (SVs); novel annotation and filtering mechanisms for SVs; and how the results of SV analyses contribute to studies of inherited diseases.
- Prerequisites _Basic data processing skills; ideally, some experience with visualisation engines and simple web apps; basic knowledge in biology or genetics preferred