Biomedical engineering research internships

Supervisors: Dr Ann-Na Cho, Ms Shihui Chen    

Eligibility:

• Demonstrated proficiency in tissue culture techniques with extensive hands-on laboratory experience
• Completed relevant coursework in Neuroscience, Biomanufacturing, and Tissue Engineering.
• Motivated to contribute to the development of humanised and miniaturised organ models (e.g., cerebral and cortical organoids) advanced with biofabrication as alternatives to animal-based research.
• Full availability to undertake daily laboratory work on campus over the VRI research period.
• Proven ability to conduct scientific literature reviews at an academic level.

Project Description:

Biomedical science is rapidly advancing in the development of complex in vitro models of the human brain using stem cell-derived brain organoids. However, conventional organoid systems often lack vascularisation, limiting nutrient delivery, long-term viability, and the ability to reproduce physiological brain function.

This project aims to biofabricate vascularised human brain models that better replicate the native brain microenvironment, including relevant cellular composition, spatial organisation, and functional characteristics. By integrating novel tissue engineering strategies, and stem cell technologies, the study will enhance organoid maturation, improve perfusion-like support, and increase model consistency for downstream experiments.

Over the course of the internship, the student will assist with stem cell culture and neural differentiation, preparation of bioinks/biomaterials, and fabrication of vascularised constructs. Key activities include viability and maturation assays, structural characterisation, and functional readouts. The outcomes will deliver a proof-of-concept vascularised brain organoid platform that supports more predictive disease modeling and therapeutic testing, contributing to translational neurobiology and precision medicine.

Requirement to be on campus: 

Yes *dependent on government’s health advice.

Supervisors: Dr Ann-Na Cho, Mr Henry Howard

Eligibility:  

• Demonstrated proficiency in tissue culture techniques with extensive hands-on laboratory experience
• Completed relevant coursework in Neuroscience, Biomanufacturing, and Tissue Engineering.
• Motivated to contribute to the development of humanised and miniaturised organ models (e.g., cerebral and cortical organoids) advanced with biofabrication as alternatives to animal-based research.
• Full availability to undertake daily laboratory work on campus over the VRI research period.
• Proven ability to conduct scientific literature reviews at an academic level.   

Project Description:

Lab-grown human cortex (“biobrain”) models are emerging as a powerful way to study brain development and disease in a human-relevant system. However, many cerebral organoid platforms emphasise cellular composition alone and lack engineered microenvironments and real-time functional readouts, limiting maturation, and circuit formation.

This project aims to build a Bioelectrode-Biobrain-on-Chip (BBoC) platform by combining cerebral organoids with advanced bioelectrodes. The goal is to support more physiologic tissue architecture and capture electrical network activity as organoids mature and respond to perturbations.

Over the course of the internship, the student will gain experience in culturing iPSC-derived cerebral organoids, integrating them into chips, and performing bioelectrode recordings. Viral exposure experiments will be conducted under approved protocols to quantify how infection alters neural circuit activity, viability, and molecular markers, alongside pilot therapeutic screening. The outcomes will deliver proof-of-concept for an electrically readable human brain infection model to study viral neuropathogenesis and accelerate drug discovery for precision medicine.

Requirement to be on campus: Yes*dependent on government’s health advice

Supervisors: Dr Ann-Na Cho, Ms Summer Cao

Eligibility: 

• Demonstrated proficiency in tissue culture techniques with extensive hands-on laboratory experience
• Completed relevant coursework in Neuroscience, Biomanufacturing, and Tissue Engineering.
• Motivated to contribute to the development of humanised and miniaturised organ models (e.g., cerebral, cortical, midbrain organoids) as alternatives to animal-based research.
• Full availability to undertake daily laboratory work on campus over the VRI research period.
• Proven ability to conduct scientific literature reviews at an academic level.

Project Description:

Rapid progress in induced pluripotent stem cell (iPSC) technology now enables the creation of lab-grown, three-dimensional human brain organoids that self-organise into functional neural tissue. Building on this breakthrough, advanced “assembloid” methods physically integrate distinct brain-region organoids to form connected brain circuits, capturing key features of neuronal crosstalk and network dynamics.

This project aims to build lab-grown human brain circuits on an organ-on-chip platform to create a scalable, patient-specific model of psychiatric disorders and a reliable workflow for drug screening. Region-specific brain organoids will be assembled into connected “assembloid” circuits, then interfaced with microfluidics to control the microenvironment and deliver drugs with precision.

Over the course of the internship, the student will gain experience culturing iPSC-derived brain organoids, assembling circuit-forming assembloids, integrating them into chips, and running pilot drug-screening assays. The outcomes will deliver a proof-of-concept brain-circuit screening pipeline, supporting personalised therapeutic discovery and next-generation preclinical testing for psychiatric medicines.

Requirement to be on campus: Yes*dependent on government’s health advice

Supervisors: Dr Ann-Na Cho, Shihui Chen

Eligibility: 

• Completed relevant coursework in Biomedical Engineering, Computer Science, Data Science, Bioinformatics, Mathematics, or related disciplines.
• Experience with Python, R, MATLAB, or equivalent programming environments.
• Strong interest in artificial intelligence, machine learning, and biomedical data analysis.
• Ability to conduct scientific literature reviews independently.
• Prior experience in image analysis, statistics, or computational modelling is desirable

Project Description:

Human brain organoids and brain-on-chip systems generate vast amounts of imaging, molecular, and functional data. This project aims to develop machine learning approaches that predict neural circuit phenotypes from multimodal human microphysiological system (MPS) datasets. Students will work with brain organoid and brain-on-chip datasets including fluorescence imaging, multiplex phenotyping, electrophysiology, and transcriptomic outputs. Using AI and data analytics pipelines, they will explore how cellular and structural features can be used to predict functional neural network behaviour and disease-associated phenotypes. The project provides experience in biomedical data science, image analysis, machine learning, and human brain model technologies, contributing to AI-enabled platforms for neurological disease modelling, precision medicine, and drug discovery.

Requirement to be on campus: No 

 

Supervisor: Prof Wei Chen, Dr Jia Liu

Eligibility: Background in biomedical engineering, neuroscience, computer science, or related field. Basic programming skills (Python/Matlab) desirable.

Project Description:

This project investigates how brain activity during sleep is linked to early cognitive changes associated with dementia. Using an existing multi-modal dataset from the Healthy Brain Ageing Clinic, including electroencephalography (EEG), sleep stage recordings, and cognitive assessments, you will apply data processing and statistical analysis to identify sleep–brain patterns that may serve as early biomarkers of dementia-related decline.

You will learn EEG signal analysis, sleep staging, and data visualisation techniques, as well as gain insights into neurodegenerative research. The outcomes may help shape future early detection tools for dementia.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Prof Wei Chen, Dr Jie Yang

Eligibility: Background in biomedical engineering, data science, computer science, statistics, or a related field. Experience with Python and basic machine learning is desirable. Familiarity with physiological signal analysis, deep learning, or weakly supervised learning would be advantageous but is not essential.

Project Description:

Polysomnography (PSG) captures synchronised neurophysiological, ocular, muscular, cardiac and respiratory signals that may provide complementary information relevant to brain health and neurodegenerative disease. However, available clinical and research datasets often contain limited, coarse-grained or noisy diagnostic and cognitive labels. This project will develop weakly supervised machine learning models for predicting neurodegenerative disease-related outcomes from multimodal PSG data. The student will investigate weak-label design and aggregation, multimodal representation learning and model training, and compare weakly supervised approaches with fully supervised and semi-supervised baselines. Sleep-stage annotations may be incorporated as auxiliary supervision. Models will be evaluated in terms of discrimination, calibration, robustness and interpretability, including the contribution of different physiological modalities to prediction. The project will provide training in rigorous machine-learning methodology, reproducible analysis and critical evaluation of label quality, while supporting the development of reliable computational tools for sleep and neurodegeneration research.

Requirement to be on campus:  *Yes, dependent on government’s health advice.

Supervisors: Prof Wei Chen, Linkai Tao

Eligibility:

We are looking for students who:

• Have a basic background in biomedical engineering or related fields
• Possess foundational knowledge of deep learning
• Are comfortable using Python for data processing and model development

More importantly, we hope you are someone who:

• Is curious about the future of human–computer interaction
• Has a passion for innovation and unconventional thinking
• Enjoys observing the world and questioning how things work

Project Description:

How Will Humans Interact with Information in the Future?

Humans naturally use eye movements to select and acquire visual information. However, modern human–computer interaction still relies largely on hand–eye coordination: we use keyboards, mice, or touchscreens to issue commands, while information is ultimately received through vision.

As the amount of digital information continues to grow, more natural and efficient interaction methods are needed. Using eye movements to directly control how information is presented may provide a promising alternative.

Advances in AI-based activity recognition now allow computers to better understand human behaviour. Meanwhile, bioelectrical signals such as electrooculography (EOG) and electromyography (EMG) provide new ways for machines to interpret human intention.

In this project, students will learn to acquire and process EOG and EMG signals, develop deep-learning-based activity recognition models, and build a “what you see is what you control” interactive prototype for exploring novel human–computer interaction.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Mike (Chia Lun) Wu, Yuta Liu

Eligibility: Some wet-lab experience is preferred. Applicants should have basic knowledge in biomedical engineering, cell biology, or a related discipline, as well as a basic understanding of fluid mechanics relevant to microfluidic systems. Experience or interest in microfluidics, blood handling and microscopy would be beneficial. Applicants must be eligible to work with human blood samples, or be willing to complete the required biosafety training, health check, and vaccination requirements before commencing the project.

Project Description:

Arterial narrowing contributes to heart attack and stroke, but how the extreme and disturbed blood-flow forces created by a atherosclerotic stenosis affect red blood cells (RBCs) remains poorly understood. This project will investigate whether repeated exposure to abnormal shear stress biomechanically primes RBCs, producing persistent functional changes that make them fragile, adhesive and less able to pass through downstream capillaries. The student will use a Kinexus rheometer to apply controlled shear priming, then perfuse RBCs through microfluidic chips that recreate graded arterial stenoses and 5 µm capillaries. High-speed microscopy and quantitative image analysis will track individual cells before and after capillary entry, measuring transit velocity, entry delay, retention, accumulation, adhesion, micro-channel obstruction and susceptibility to haemolysis. By revealing how arterial disease may trigger downstream microvascular blockage, the project could identify new mechanical biomarkers and therapeutic targets. The student will gain interdisciplinary experience spanning blood biology, biomechanics, microfluidics and biomedical imaging.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Dr Tasneem Rahman and Prof Alistair McEwan

Eligibility: WAM≥75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

This project aims to develop data-driven approaches to predict and understand changes in cognitive performance during prolonged spaceflight conditions. Using publicly available datasets such as NASA’s cognition battery and others, the project investigates how physiological and behavioural changes relate to psychomotor performance over time. The intern will contribute to data preparation, exploratory analysis, temporal feature engineering, statistical modelling, visualisation, and evaluation of machine learning approaches for predicting cognitive performance. The intern will also conduct a systematic survey and catalogue of publicly accessible cognitive datasets across major space agency repositories, documenting relevant variables, study designs, and data accessibility for future research integration. The project contributes to computational frameworks for longitudinal cognitive performance monitoring without requiring new data collection. Findings may inform astronaut cognitive risk monitoring and resilience assessment during long-duration space missions, with broader relevance to other high-stress operational environments including aviation, defence and healthcare.

Requirement to be on campus: Flexible

Supervisors: Dr Andre Kyme, Francisco Enriquez, Prof Steven Meikle

Eligibility:  Strong capability in math and computing units, will be evaluated on a case-by-case for suitability.

Note: Ongoing funding throughout the 8-week program will be contingent on fortnightly probationary and milestone requirements being met.Project Description:

Project Description:

Positron emission tomography (PET) is a functional imaging technique that allows us to investigate brain function and neurochemistry in health and disease. However, conventional PET imaging of small-animal models (e.g., mice and rats) typically requires anaesthesia to avoid motion artefacts, which limits the possibility of simultaneously investigating brain function and animal behaviour. This ultimately constrains our understanding of how the mammalian brain works.

We have developed a unique PET system capable of imaging the brain of small animals while they are awake and freely moving: the Motion-Adaptive Molecular and Behavioural Observation (MAMBO)-PET scanner.

In this project, you will contribute to the development of a state-of-the-art image reconstruction algorithm to improve system performance, and support the first experimental animal studies using MAMBO-PET. The project is well suited to a student with a good understanding of mathematics and computer programming. Experience in C++ is not required but would be highly valued. 

Requirement to be on campus: *Yes, dependent on government’s health advice. The selected student is expected to commit full-time to the project for the duration of the scholarship. Some work may be completed remotely, but this will be based on approval by the research team.

Supervisors: Dr Clara Tran , Dr Stuart Fraser (Culturon Pty Ltd), Professor Marcela Bilek 

Eligibility: WAM≥75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

Progesterone is an important reproductive hormone that provides valuable information about the reproductive status of dairy cows. Our team is developing portable biosensors to quantify progesterone directly on dairy farms, providing farmers with rapid information to support reproductive and livestock management.

One of the challenges in detecting progesterone in milk is its strong association with milk fat which can limit its accessibility to the capture molecules used in biosensors. This VRI project aims to investigate methods for disrupting milk fat globules and releasing progesterone to improve its accessibility and detection.

The student will undertake hands-on laboratory experiments involving wet chemistry and milk sample preparation and will work alongside PhD students involved in the broader progesterone biosensor project. The project will provide practical experience in biosensors, biomaterials, analytical techniques, sample preparation, and experimental research.

This project is suitable for undergraduate students interested in biotechnology, biomedical engineering, chemistry, materials science, biosensing, or agricultural technologies.

Requirement to be on campus: *Yes, dependent on government’s health advice

Supervisors: Prof Antonio Tricoli, Mahroo Baharfar

Eligibility: WAM≥75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description:

Currently, green energy industrial processes rely heavily on photo- and electro-catalysts, which face significant challenges when scaling to an industrial level. There is a critical lack of scalable and economically feasible reactors capable of addressing this gap for sustainable applications.

The student will lead the design and manage the manufacturing of a novel, versatile liquid metal aerosol reactor system. The reactor will be capable of in-situ aerosol production in the gas phase. Throughout the project, the student will specifically focus on engineering a reactor designed to facilitate a reaction between carbon dioxide and liquid gallium, with the potential to scale to industrial applications.

Using liquid metal has distinct benefits due to the highly fluidic and dynamic nature of liquid catalysts that typically outperform fixed solid-phase catalysts. Gallium eutectic alloys are liquid at room temperature, enabling the possibility of versatile and simple integration into existing industrial infrastructure, such as implementation in exhaust stacks where carbon dioxide is currently released into atmosphere.

Requirement to be on campus: No (May be required from time to time)