Biomedical engineering research internships

Supervisors: Prof Alistair McEwan, Michael Wong,  Frank Iorfino, Tessa Garside

Eligibility:  Students in Biomedical Engineering, Computer Science, Software Engineering, Electrical Engineering, Mechatronics, or related fields. Experience with Python, machine learning, signal analysis, or health data is desirable.

Project Description:

Emerging cortisol biosensors can generate rich continuous data, but translating these raw electrochemical signals into meaningful information for clinicians remains a major challenge.

This project will develop an early-stage AI and signal processing pipeline to transform cortisol sensor outputs into interpretable and potentially predictive metrics relevant to mental health and circadian physiology. In collaboration with the Brain and Mind Centre and Royal North Shore Hospital, , the student will work with existing electrochemical and biomarker datasets to examine how features such as dynamic signal changes, time-series trends, and area-under-curve behaviour may relate to clinically relevant states.

Tasks may include preprocessing biosensor data, exploratory modelling, feature engineering, and development of visual outputs or dashboards to support future clinical interpretation. The project offers experience at the interface of biomedical engineering, machine learning, digital mental health, and translational biosensing.

Requirement to be on campus: No

Supervisor: Supervisors: Dr Mike (Chia Lun) Wu, Allan Sun

Eligibility: 

    -     Some wet-lab experience is preferred.

     -    Applicants should have basic biological knowledge relevant to biomedical engineering            and be eligible to work with human blood samples, or willing to undergo the required            health check (e.g., vaccination).

Project Description:

Glioblastoma (GBM) is an aggressive brain tumour that strongly perturbs the local vascular environment. Our preliminary data suggest that tumour-conditioned environments in vitro and our mouse GBM model may trigger red blood cell damage and a microvascular thrombotic response. We hypothesise that this thrombotic response may in turn promote cancer growth by worsening local perfusion failure and shaping a tumour-supportive microenvironment. If so, early detection of these blood changes could help stratify disease behaviour and inform treatment strategies.

This project will support early development of a paper-based lateral flow assay, ClotCheck-GBM, designed to detect these tumour-induced blood signatures (RBC membrane damage and derived extracellular vesicles). The student will perform initial biological testing of human blood exposed to cancer cells on the paper platform, analyse changes in flow and visible assay patterns, and contribute to iterative engineering design of the device by refining platform features based on the biological results obtained.

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

Supervisors: Prof Omid Kavehei

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:

Neuromorphic software design takes ideas from the way the human brain works. It has become more important as artificial intelligence continues to grow. Many current AI systems depend on large central computers that use a lot of power and must constantly communicate with remote servers. In many medical situations this is not practical. Neuromorphic computing offers a different approach. It aims to process information in ways that are similar to how the brain handles signals. This can allow systems to work faster, use less power, and react quickly when new information appears.     

One useful use of this idea is processing data in real time at the edge of intelligent systems. Edge computing means that data is processed on the device itself, or very close to where the data is created, instead of sending it to distant cloud servers. In healthcare this can be very valuable. Signals from the body such as brain activity, heart rate, or other biological measurements can be analysed directly on the device. This reduces delays, lowers the energy needed for wireless communication, and helps protect patient privacy because sensitive data does not always need to be sent across networks. Communication with external systems can still happen when needed, but it does not have to happen all the time. The project studies how neuromorphic systems can be used in edge based biomedical devices. The aim is to combine artificial intelligence detection with automatic feedback to create a closed loop system. The system would analyse electroencephalography, or EEG, signals from the brain in real time and look for patterns linked to epileptic seizures. EEG data would be processed directly on the device using brain inspired methods that can detect unusual patterns of activity. When these patterns appear, the system could respond with a form of controlled stimulation that may help reduce or control seizure activity. 

Combining neuromorphic computing with closed loop medical systems could make treatment more efficient and more effective. The system can react quickly when important brain signals appear, while still using very little power during quiet periods. Processing data on the device also reduces the need for constant connection to outside computers. This could support the development of portable or implantable medical devices that work continuously in everyday life.    

Developing this kind of technology is an important goal in modern biomedical engineering. As AI becomes part of more medical devices, there is a growing need for systems that are efficient, responsive, and able to handle sensitive health data safely. Neuromorphic edge computing offers a promising path towards medical devices that can monitor brain activity, understand complex signals from the body, and respond quickly when help is needed.  

Requirement to be on campus: Not necessary but preferred

Supervisors: Prof Wei Chen, Dr Jia Liu, Dr Shawn Kong, Prof Sharon Naismith

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. detection tools for dementia.

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

Supervisor: Prof Wei Chen, Dr Jia Liu, Dr Aaron Lam, Prof Sharon Naismith

Eligibility: System design and circuit design skills; Programming skills

Project Description:

This project aims to design an auditory stimulation paradigm specifically for tonic rapid eye movement (REM) sleep. Tonic REM is characterised by relatively stable neural activity and fewer bursts of eye movements compared with phasic REM, making it a more suitable and safer period for controlled stimulation. The project will focus on developing a structured experimental paradigm for delivering brief auditory stimuli during tonic REM, including consideration of stimulus type, timing, intensity, delivery strategy, and methods to minimise sleep disruption. Through reviewing the current literature and integrating REM-specific physiological characteristics, this project will establish a feasible paradigm that can support future experimental studies on tonic REM-targeted stimulation.

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 developmen

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?

For decades, human–computer interaction has relied on hand–eye coordination: we issue commands with hand movements, systems respond, and we receive information visually. This approach partly arose because capturing eye activity with low-power technologies was difficult. While effective, it does not fully match how humans naturally interact with information and often requires people to adapt to machines. In an era of exploding information, we need to rethink this paradigm.

With advances in AI-driven activity recognition, computers are becoming capable of understanding human behavior directly. At the same time, bioelectrical signals such as Electrooculography (EOG) and Electromyography (EMG) provide powerful channels for machines to perceive human intent.

In this project, you will learn to acquire and process EOG and EMG signals, build activity recognition models using deep learning, and develop a “what-you-see-is-what-you-get” interaction prototype system.

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

Supervisors: Prof Wei Chen, Jia Liu, Linkai Tao

Eligibility:

We are looking for students who:

Have a basic background in biomedical engineering, physiological signals or related fields

    -    Have programming skills (Python/Matlab).

    -    Are curious about the future of cardiovascular disease monitoring

    -    Have a passion for innovation and unconventional thinking

Project Description:

Neurodegenerative diseases such as Alzheimer's disease are associated with progressive cognitive decline and changes in neural function. Increasing evidence suggests that abnormalities in eye movement can reflect dysfunction in brain regions involved in cognition and motor control. Electrooculography (EOG) records electrical potential changes generated by eye movements and offers a simple, non-invasive method for capturing oculomotor activity. Compared with camera-based eye tracking or imaging techniques such as Magnetic Resonance Imaging, EOG is low-cost, portable, and well suited for wearable and long-term monitoring applications.

This short project will explore the potential of EOG signals for monitoring the progression of neurodegenerative conditions. Students will investigate eye-movement features extracted from EOG recordings and evaluate their relationship with neurological changes. The project will also consider how wearable EOG systems could support future home-based monitoring and assistive care for neurodegenerative disease management.

Requirement to be on campus: No

Supervisors: Prof Wei Chen, Dr Jia Liu, Linkai Tao

Eligibility: We are looking for students who:

    -    Have a basic background in biomedical engineering, physiological signals or related fields

    -    Have programming skills (Python/Matlab).

    -    Are curious about the future of cardiovascular disease monitoring

    -    Have a passion for innovation and unconventional thinking

Project Description:

Cardiovascular diseases are one of the leading causes of death worldwide. Many heart conditions develop slowly and may not show clear symptoms until serious damage has already occurred. Continuous and reliable monitoring helps detect early warning signs, enabling timely diagnosis and treatment. This can prevent complications such as heart attack, stroke, or heart failure. Effective monitoring also allows doctors to track treatment outcomes and adjust therapies for each patient. In addition, long-term monitoring supports preventive care by identifying risk factors such as irregular heart rhythms or abnormal blood pressure. Improving cardiovascular disease monitoring is therefore essential for reducing mortality, improving quality of life, and lowering healthcare costs.

In this project, advanced sensor systems and state-of-the-art physiological signal analysis methods will be explored. Design concept of novel intelligent sensor system for long term home monitoring will be proposed.

Requirement to be on campus: No

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 perio

    -    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 6-week 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, Immunology, and Biomanufacturing.

    -    Motivated to contribute to the development of humanised and miniaturised organ            models (e.g., cortical organoids) powered with bioelectrode interface.

    -    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 6-week 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 6-week 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 Jinglei Lv

Eligibility: 

    -    Basic skills with programming.

    -    Basic knowledge about medical image.

    -    Self-motivation, curiosity about research and passion to succeed

Project Description:

Artificial Intelligence (AI) is transforming neuroscience by enabling new ways to model brain structure, function, and cognition. The next generation of AI—foundation models and LLMs—offer unprecedented opportunities to integrate multimodal data (e.g., neuroimaging, genomics, clinical records, behavioural data) and generate personalised, interpretable insights into brain health.

This project will explore how LLMs and foundation models can be tailored for neuroscience, including:

• Neuroimaging analysis: Using foundation models to learn shared representations across MRI, fMRI, DTI, and PET for improved diagnosis and prognosis of brain disorders.
• Knowledge integration: Applying LLMs to synthesise biomedical literature, clinical notes, and multi-omics data to uncover new disease mechanisms.
• Digital brain twins: Developing personalised foundation models that capture individual variability, predict disease trajectories, and guide therapeutic strategies.
• Interpretability and trust: Designing explainable AI tools that link model predictions to neurobiological mechanisms, ensuring clinical reliability.

By uniting AI innovation with neuroscience, the project aims to build a new generation of computational tools that advance understanding of the healthy and disordered brain while driving precision medicine in brain health.

Requirement to be on campus: No 

Supervisor: Dr Jinglei Lv

Eligibility:

    -    Basic skills with programming.

    -    Basic knowledge about medical data.

    -    Self-motivation, curiosity about research and passion to succeed. 

Project Description:

Modern biomedical research increasingly relies on complex multimodal datasets—including medical imaging, genomics, blood biomarkers, and behavioural assessments. However, many clinicians lack the computational expertise required to analyse these heterogeneous data sources, limiting their ability to generate research insights from rapidly growing clinical datasets.

This project proposes the development of a trustworthy agentic AI system that assists clinicians in conducting biomedical data analysis and research. The system will leverage large language models integrated with domain-specific software libraries and community knowledge bases to autonomously plan, execute, and interpret analytical workflows. It will support tasks such as medical image processing, multimodal data integration, statistical testing, and reproducible research pipelines.

The novelty lies in combining agentic AI with domain-aware biomedical analytics tools to create a transparent and reliable research assistant tailored for clinicians. By lowering technical barriers to advanced data analysis, the platform has the potential to accelerate clinical discovery and enable more effective translation of complex biomedical datasets into actionable insights.

Requirement to be on campus: No