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
In this project the candidate will use the multi-omic data to identify fibroblast-specific genomic regulatory regions and determine their target genes. These regions will be overlapped with GWAS loci to nominate risk loci and associated genes for future functional validation studies.

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

What you will learn The student will leverage their computer science expertise to construct a dynamic workflow framework that automates data pre-processing, computation and analysis, and visualization of MSI data, whilst also learning about the generation and interpretation of large biological 'omics datasets and their application to the study and treatment of solid cancers.

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
Inherited disease studies aim to uncover the causes and mechanisms of genetic disorders. The search for candidate genetic variants explaining these diseases requires enormous amounts of data. Long-read sequencing can detect thousands of structural variants (SVs) in each individual. Unlike single-nucleotide variants, SVs exhibit substantial variability in size, structure, sequence context, and effects. However, most commonly occurring SV cases can be encoded using logic programming, a paradigm in which new information is derived through predicate-based rules. When evaluated on input data, such logic programs can generate a complete annotation space describing patient SVs in accordance with the specified rules, helping us to understand their functions. Both inputs and outputs are stored in predicate-level text files, with variants and other genomic elements represented by unique identifiers. There is a need for new tools to visualise and interact with this multidimensional space to aid variant curators in detecting patterns of how SVs affect the genome and contribute to disease.

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

5. How Hard Can Immune Cells Punch?
We have developed methods that measure the forces that cells use to move through tissue and interact. This includes watching the precisely positioned femtoscale forces that cells use migrate through our bodies. Now we want to understand what happens when different types of cells meet each other. Specifically, we want you to conduct experiments that use this technique to watch the cell types that weaponise force: T Cells and NK Cells.

This research helps us understand fundamental processes in development, immune responses, and disease - helping us to uncover how the cells of the immune system weaponise forces.

What you will learn
You will learn to use advanced microscopes to watch cells moving in 3D, and apply our computer vision tools to measure forces between different cell types. You'll discover how cells coordinate their movements when they're near each other, and whether different cell combinations create unique force patterns.

Prerequisites A love of biology and math; Interest in solving the biggest unsolved mysteries in biology
 

6. Unveiling Hidden Mechanisms in Solid Tumor CAR-T Therapy: A Bilingual Analysis of Clinical Trials and Novel Mechanistic Insights Across Eastern and Western Research
This 10-week undergraduate research project maps the global CAR-T landscape for solid tumours through bilingual analysis of English and Chinese clinical literature, complementing laboratory CAR-T expansion and phenotyping work. The student will analyse clinical trials from Western ) and Chinese (ChiCTR) databases using translation tools, systematically tracking novel mechanistic insights that appear differentially across languages. The project focuses on identifying two categories of hidden mechanisms: metabolic resistance patterns reported primarily in Chinese literature, and combination therapy synergies unique to Asian standard-of-care. Based on findings, the student will design and conduct wet lab experiments testing whether metabolic signatures (e.g., lactate-induced exhaustion, glucose competition effects) identified in Chinese clinical reports can be recapitulated in their CAR-T culture system.

Deliverables include a bilingual trial database, mechanism divergence matrix, experimental validation data, and report correlating laboratory phenotypes with clinical outcomes. This project uniquely bridges cross-cultural clinical analysis with experimental validation, potentially revealing therapeutic insights hidden by language barriers.

What you will learn
The student will gain experience of a simulated research cycle — from systematic literature analysis to experimental validation — gaining a interdisciplinary perspective that integrates global clinical trial data with translational CAR-T biology. Specifically, Clinical Research Literacy (navigating international trial registries such as , ChiCTR; comparative analysis of trial design and endpoints; use of translation tools for bilingual scientific interpretation) as well as Laboratory Skills (T cell expansion and phenotyping, flow cytometry). They will hone their Data Integration & Reporting skills by preparing a comprehensive written report synthesising literature review and laboratory findings.
Prerequisites Native proficiency in English and Chinese; enjoys systematic data extraction/ automation; interested in cancer immunotherapy
 

7. Modeling circadian dysregulation in triple-negative breast cancer using machine learning
Cancer cells exploit non-genetic, embryonically conserved plasticity programs to switch between states and evade therapy. The epithelial-to-mesenchymal transition (EMT) drives dedifferentiation into the highly aggressive therapy-resistant cancer stem cells (CSCs), while the reverse MET process restores drug-sensitive non-CSCs. We aim to resolve the temporal molecular dynamics of these transitions and uncover clinically translatable strategies to re-sensitize CSCs.

Circadian rhythms regulate ~24-hour gene expression cycles, including cell cycle regulators essential for genome stability. Embryonic stem cells divide without circadian input, acquiring rhythmic coordination only upon differentiation. Using this paradigm, we hypothesize that dedifferentiated CSCs lack robust circadian rhythms, whereas non-CSCs regain circadian regulation. These differences in circadian dynamics may influence therapy response and metastatic potential, but remain unexplored. Using temporal scRNA-seq datasets of CSCs and non-CSCs, we will apply machine learning–based trajectory and topological analyses to identify circadian targets that can be therapeutically exploited to resensitise CSCs to existing therapy.
 

What you will learn
The student will be working with single cell RNA seq datasets that have been collected at high temporal resolution. The student will be using PHATE for dimensionality reduction, MIOflow to trace trajectories to key cell fates and modelling circadian rhythm in across these trajectories. We have a working pipeline (Cflows) that is ready to be applied to this newly acquired dataset by the student. Depending on the structure of the data we may use reeb graphs to refine this model. Overall this project is at the intersection of identifying novel biology driving aggressive cell states in cancer and using machine learning tools.
 

Prerequisites Familiarity with programming in python, ML libraries like scikit-learn and PyTorch
 

ABOUT GARVAN

Garvan Institute of Medical Research is an independent Medical Research Institute (MRI) in Sydney, delivering scientific and clinical impact on a global basis and in partnership with organisations that share our vision. We are proud to be one of Australia's largest and most highly regarded MRI's.

Our vision is global leadership in discoveries to impact and our enduring purpose is to impact human health, by harnessing information encoded in our genome.

We seek to see our world-class discovery research achieve life-changing impacts, not only for individual patients with rare diseases, but for the many thousands affected by complex, common disease.

Garvan promotes a diverse workplace and is committed to the principles of equity, diversity, inclusion and belonging. We are always looking for culture 'add', not culture 'fit' and are building diverse teams with great sets of complementary styles and skills to help deliver our important work effectively.

HOW TO APPLY

To apply you must complete both Parts as below:

Part One:

Your application via the Garvan Careers Site/Workday should include:

• Copy of your CV/resume [no more than five (5) pages]

• Cover letter outlining which project(s) you are applying for [one page only]

• Copy of your academic transcript/s

[Note - Our system requires these documents to be compiled into one PDF document]

Part Two:

In addition to submitting your application via Workday, please complete the Student Applicant Form at:

Note:

All applications must be submitted via the Garvan Careers site [Workday].

Applications from other sites/channels will not be considered.

Incomplete applications or applications without supporting documents will not be assessed.

CLOSING DATE

The position will remain open until filled. We will be reviewing applications as they are received, and so we encourage you to submit your application as soon as possible.

We aim to have positions filled by mid-October 2025 for project commencement in Mid-November 2025. All applicants will be notified of the outcome of their application by mid-October 2025.