Explore a range of biomedical engineering research internships to complete as part of your degree during the semester break.
The following internships listed are due to take place across the Summer break.
Applications open on 10th September and close 30th September 2025.
Supervisor: Prof. Ken-Tye Yong
Eligibility: Students from Mechatronics, Electrical, Biomedical Engineering, Computer Science, or Physics (UG/Hons).
Essential: embedded coding (Arduino/C or MicroPython), soldering/prototyping, simple CAD/3D printing, data logging, clear documentation.
Desirable: BLE/LoRa comms, KiCad/PCB design, audio signal processing/ML, experience handling invertebrates. All safety and welfare training provided.
Project Description:
Goal: Demonstrate a low-mass, multi-modal sensor backpack on the native Giant Burrowing Cockroach (Macropanesthia rhinoceros) to prove feasibility for subterranean reconnaissance and disaster sensing. Students will (1) assemble an ESP32-C3/nRF52840 board with MEMS mic, temp-humidity, light and IMU; (2) design a <6 g 3D-printed pronotum shell; (3) stream BLE telemetry and log data; (4) evaluate motion and environmental cues in a rubble/soil testbed; and (5) (optional) add a current-limited two-channel stimulator for left/right guidance. Expected outputs: working demo, code repo and short report; feasibility data for AU/US provisional patent claims on species-specific mounting, soil-aware sensing and control. Training on invertebrate welfare and lab safety provided.
Requirement to be on campus: Yes – lab-based prototyping and testing (rubble/soil testbed in labs) *dependent on government’s health advice
Supervisor: Dr Ann-Na Cho, Shuhui Chen
Eligibility:
Project Description:
Biomedical science is rapidly progressing in the development of complex in vitro models of the human brain using brain organoids derived from stem cells. Conventional models often lack vascularisation and tissue-level architecture, limiting their ability to replicate physiological brain function and disease mechanisms.
This project focuses on the biofabrication of vascularised human brain models that more accurately reflect the native microenvironment, including cellular composition, spatial organisation, and functional characteristics. By integrating novel biomaterials, tissue engineering strategies, and stem cell technologies, the study aims to enhance organoid maturation and functionality.
These advanced models will be utilised to investigate the mechanisms underpinning neurodegeneration and to assess the effectiveness of targeted nanomedicine-based therapies. The platform also holds potential for studying viral neuropathogenesis. This research is well-suited to students interested in stem cell engineering, neurobiology, biofabrication, and translational applications in precision medicine.
Requirement to be on campus: Yes*dependent on government’s health advice
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.
Requirement to be on campus: No
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 explores how brain activity and sleep patterns relate to mental health outcomes, such as stress, anxiety, and mood. Using the Healthy Brain Ageing Clinic dataset, which combines electroencephalography (EEG), sleep monitoring, and validated mental health questionnaires, you will analyse neural and sleep-related markers linked to psychological wellbeing.
You will learn EEG preprocessing, spectral analysis, and understand psychophysiological measures of mental health. The work will provide valuable research experience in neuroengineering and mental health analytics, with potential applications in early detection and intervention strategies.
Requirement to be on campus: No
Supervisor: Prof. Wei Chen
Eligibility:
Project Description:
This project recruits students to design, build, and validate an EEG+IMU wearable hardware. Work spans schematic capture and PCB layout for a low-noise EEG AFE (input protection, DRL/shielding, ESD), a 6–9-axis IMU, a MCU, and a power subsystem. Students will perform board bring-up, quantify noise/CMRR and power, and implement firmware drivers plus a shared timebase for EEG/IMU synchronization. This project could be positioned as Phase-0 for an Honours/Master’s thesis or capstone; applicants could commit to continuing and maturing the system as their thesis/capstone in subsequent terms.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Prof. Wei Chen
Eligibility:
Project Description:
This project recruits students to design, build, and validate an EEG+IMU wearable hardware. Work spans schematic capture and PCB layout for a low-noise EEG AFE (input protection, DRL/shielding, ESD), a 6–9-axis IMU, a MCU, and a power subsystem. Students will perform board bring-up, quantify noise/CMRR and power, and implement firmware drivers plus a shared timebase for EEG/IMU synchronization.
This project could be positioned as Phase-0 for an Honours/Master’s thesis or capstone; applicants could commit to continuing and maturing the system as their thesis/capstone in subsequent terms.
Requirement to be on campus: Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Prof. Wei Chen, Dr. Jia Liu
Eligibility:
Project Description:
This project evaluates whether single-channel, low-power wearable EEG systems can reliably detect event-related potentials (ERPs)—brain responses to specific sensory or cognitive events. You will help design and test minimal ERP paradigms such as auditory/visual oddball tasks and compact Go/No-Go protocols. The study will optimise electrode site selection, improve contact quality, and explore the trade-offs between sampling rate and signal-to-noise ratio (SNR).
This research bridges engineering and cognitive neuroscience, offering skills in experimental design, wearable neurotechnology, and EEG data acquisition. Your findings could inform the development of portable cognitive assessment tools for field or clinical use.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Prof Wei Chen, Dr Jia Liu, Dr Aaron Lam, Pror Sharon Naismith
Eligibility: Background in electronic engineering, biomedical engineering, neuroscience, computer science, or related field. Basic programming skills (Python/Matlab) desirable.
Project Description:
Rapid eye movement (REM) sleep is characterised by vivid dreaming and wake-like brain activity. REM sleep plays a critical role in emotional regulation, memory consolidation, and brain plasticity. Ageing is associated with marked changes in REM sleep including reduced REM duration, fragmentation of REM sleep periods, and a slowing of brain activity during REM sleep.
In this project, you will design the application of acoustic stimulation into REM sleep, to enhance REM-specific neural processes that support cognition and dream generation in older adults. More specifically, this project will investigate the effects of REM sleep modulation on REM EEG characteristics (e.g., spectral power), overnight memory consolidation, and dream recall.
You will learn brain stimulation, EEG signal analysis, and sleep staging, as well as gain insights into neurodegenerative research. The outcomes may help shape future regulation and therapies for dementia.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Dr Gurvinder Singh and Lois Lam
Eligibility: Applicant should have background knowledge in nanomedicine and prior experience in working with lipids, microfluidic device or chirality/circular dichroism will be highly encouraged.
Project Description:
The oral delivery of therapeutics (mRNA/drug molecules) remains a formidable challenge due to degradation in the gastrointestinal tract and poor epithelial transport. This project aims to formulate chiral lipid nanocarriers (CLNs) capable of co-encapsulating therapeutic drugs and mRNA with precise control over size, composition, and stereochemistry using microfluidic approach. By leveraging chirality as a design parameter, CLNs are engineered to exploit stereospecific interactions with cellular membranes, enhance mucosal penetration, and promote endosomal escape.
A library of CLNs will be generated using high-throughput microfluidics, enabling systematic optimisation of physicochemical parameters. Their structural and functional performance, including particle size, colloidal stability, encapsulation efficiency, and release kinetics will be rigorously characterised under simulated gastrointestinal conditions using DLS, UV–vis spectroscopy, and circular dichroism.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Prof. Wenlong Cheng, Dr Lim Wei Yap
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:
Body movement is a critical marker of wellbeing, with applications in aged care monitoring, rehabilitation, seizure detection and human-machine interaction (HMI). However, current wearable sensors for body movement monitoring are often bulky, rigid, or uncomfortable, limiting long-term use and user compliance. There is an urgent need for sensing technologies that are both robust and unobstructive in wear.
This project aims to develops a resilient, seamless, and highly stretchable strain sensor using cutting-edge, biocompatible nanomaterials. This ultrathin, skin-conformal design would ensures comfort and unobtrusive integration with the body, while excellent stretchability, durability and quick reaction time of the sensor allow precise monitoring of complex motions without degradation. The use of biocompatible materials further guarantees safety and long-term compatibility for continuous wear. By merging nanomaterial innovation with scalable fabrication, this work will establish next-generation wearable platforms for healthcare, rehabilitation, sports performance, and advanced HMI systems.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Prof. Wenlong Cheng and Dr Jayraj V. Vaghasiya
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:
Plants respond to pathogens and environmental stresses through complex immune mechanisms, but conventional methods to study these processes are often invasive and destructive. This project aims to develop a lightweight electronic tattoo (e-tattoo) that can be directly applied to leaf surfaces to monitor plant immune responses in real time. The device will use impedance spectroscopy, a non-invasive electrical sensing technique, to capture subtle changes in leaf physiology associated with immune activation. During the 8-week internship, the student will focus on fabricating simple e-tattoo prototypes using conductive inks (either conductive polymer or gold nanoparticle), optimizing their adhesion and stability on leaves, and testing their ability to record electrical signals under controlled stress conditions.
The outcomes of this project will demonstrate the feasibility of e-tattoos as tools for plant health monitoring. This approach may offer new, minimally invasive methods for studying plant defense, with potential applications in agriculture, plant biology, and environmental monitoring.
Requirement to be on campus: *Yes, dependent on government’s health advice. If circumstances change due to public health orders, the student could focus on data analysis and write a literature review on electrochemical innovations in plant sensing.
Supervisors: Dr. Sabrina Schaly, Prof. Alistair McEwan
Eligibility: Would be preferable if the student has had some exposure or experience working with people with disability.
Project Description:
Tech-Toys are custom-made and switch-adapted toys accessible for children with physical disabilities. Current market toys are expensive, inaccessible, and offer limited customisability. Tech-Toys addresses this by co-designing toys with families to ensure they meet the family’s needs. For this project, you will be helping create resources and educational materials for the website that will host the TechToys. This will include instruction manuals, resources to learn how to switch-adapt toys at home, detailed drawings with dimensions, and more.
Requirement to be on campus: No (May be required from time to time)
Supervisors: Dr. Sabrina Schaly, Prof. Alistair McEwan
Eligibility: would be ideal if the student has some previous experience with app development (any language or platform).
Project Description:
This app is designed to support communication research and assistive technology development for minimally speaking individuals. It enables caregivers to label vocalizations in real time while audio is continuously recorded in natural environments. By capturing the context and meaning of vocal sounds (requests, frustration, or delight) the app helps build a personalized dataset of expressive vocalizations. These labelled audio clips will be used to create a system for individuals to communicate without words. Over time, the system can evolve into a learning library that adapts to each person’s unique vocal expressions, improving prediction accuracy and responsiveness.
The app should be low-cost, easy to use, and designed to minimize burden on families while maximizing ecological validity. Ultimately, it aims to empower caregivers and researchers to better understand and support the communicative needs of individuals with limited verbal language.
Requirement to be on campus: No
Supervisor: Prof. Alistair McEwan
Eligibility: Proficiency in programming languages, preferably in Python.
Project Description:
This project aims to develop an AI Chatbot, integrating advanced speech recognition tools like Whisper.cpp and large language models like Llama3. The chatbot is a real-time and locally run application that detects user voice activities and transcribes them as input to Llama3. The output of the language model is then vocalised through a text-to-speech system. Thus, the AI Chatbot is a real-time audio-to-audio interface, which will be trained eventually on speech datasets collected from people with different types of speech communication disabilities. For the safeguards of the users, the Llama3 model will be contextualised in addition to retraining the model on user-specific datasets to prevent the system from generating inappropriate or harmful outputs that potentially undermine the usability of such technologies for people with speech communication disabilities.
Requirement to be on campus: No
Supervisors: Prof. Alistair McEwan, Dr Tasneem Rahman, Dr Sabrina Schaly, Monzurul Alam
Eligibility: Should ideally have interest and skills in electronics, biomedical engineering and human factors design.
Project Description:
This project addresses critical engineering challenges in neonatal transport, focusing on improving the reliability, safety, and physiological stability of mobile care environments. Transport incubators must adapt to fluctuating power systems across ambulances, aircraft, and hospitals, requiring robust power management and seamless integration with medical devices. Additionally, mechanical vibrations and motion during transit can compromise vital sign monitoring and infant comfort. We aim to develop vibration-dampening platforms, shock-absorbing mounts, and power-adaptive modules to ensure consistent device performance and minimal physiological stress.
The project will also explore wireless telemetry and environmental control systems to maintain thermal and oxygen stability. By engineering resilient, adaptable transport solutions, this initiative supports continuity of neonatal care and reduces risks during critical transfers.
Requirement to be on campus: No
Supervisors: Prof. Alistair McEwan, Antony Clements, Dr Sabrina Schaly, Monzurul Alam
Eligibility: Should ideally have interest and skills in electronics, biomedical engineering and human factors design.
Project Description:
Accessible Gaming Controller Research This project explores the development of an adaptive gaming controller designed for individuals with monoclinic movements and spasticity. The controller will integrate signal detection technologies—such as electromyography (EMG), pressure sensors, or motion tracking—to interpret subtle user inputs and involuntary muscle activity. By translating these signals into responsive gameplay actions, the device aims to overcome barriers posed by limited motor control. The design will prioritize ergonomic comfort, low activation force, and customizable input mapping. Through user-centered research and iterative prototyping, the project seeks to empower players with neuromotor challenges, enabling richer interaction and inclusion in digital gaming environments. Ultimately, this work contributes to the broader field of accessible technology and inclusive entertainment.
Requirement to be on campus: No
Supervisors: Prof. Wenlong Cheng, Dr Yan Lu
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:
Epidermal bioelectronics are transforming personalized healthcare by enabling continuous, real-time monitoring of physiological signals directly from the skin. However, existing electronic skins or tattoos (E-tattoos) are typically thin films or conductive coatings applied to the skin surface. Their integration with skin is mechanically fragile, making them prone to peeling off and unreliable long-term performance.
This project aims to overcome these limitations by developing a permanent E-tattoo, implanted directly into the dermis using a tattoo gun, similar to traditional tattooing. Specially formulated conductive nanocrystal inks will be designed to balance high conductivity with safe and effective delivery into skin tissue.
Over the course of the 8-week internship, the student will fabricate prototype permanent E-tattoo biosensors on porcine skin and systematically evaluate their performance. Key tests will include recording strain and electrochemical signals.
The outcomes will deliver proof-of-concept for a new class of in-skin bioelectronics, providing a robust and durable strategy for continuous health monitoring. This disruptive platform could pave the way for future implantable diagnostics, personalized therapies, and long-term wearable healthcare technologies.
Requirement to be on campus: Yes * If circumstances change due to public health orders, the student could focus on data analysis and write a literature review on electrochemical innovations in plant sensing.
Supervisors: Dr Tasneem Rahman, Prof. Alistair McEwan and A/Prof.Kathryn Browning Carmo
Eligibility: Students with any given experience around the field of research of neuroscience and neuroimaging, computational simulation and modelling, algorithm development will be given priority.
Project Description:
One in every 49 children will travel with a NETS (Newborn & paediatric Emergency Transport Service) team sometime during their childhood. In rural areas, this chance increases to 1 in 38. Newborn intensive care transport systems provide a mobile intensive care station for transporting critically ill newborns and young infants. The bespoke system design was launched in 2012 supporting NETS operations in Sydney, Canberra and Newcastle. An upgraded design is expected in 2025.
Modules that provide additional clinical capabilities, such as Inhaled Nitric Oxide, DC countershock, and phototherapy, can enhance these systems.These were designed and constructed in-house by NETS’ clinicians and biomedical engineers, using a design supported by aerospace and software engineers. The design was based on NETS’ clinical and operational experience overseeing thousands of emergency patient retrievals to support neonatal care without interruption during inter-hospital transport. The project aims to enhance its capability for safe services.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Dr Tasneem Rahman and Prof. Alistair McEwan
Eligibility: Students with any given experience around the field of research of neuroscience and neuroimaging, computational simulation and modelling, algorithm development will be given priority.
Project Description:
This project focuses on early mental health detection during women’s menopausal transition, utilising open-access datasets like the SWAN Study and OSF perimenopause cognition data. These datasets highlight the increased risk of depression, anxiety, sleep disturbances, and cognitive changes linked to hormonal fluctuations in perimenopause, menopause, and post menopause. By analysing variables such as hormone levels, sleep quality, life stressors, and menopausal stage, the project aims to develop a predictive model using methods like logistic regression or random forest to identify women at high risk of mental health issues early. The intervention will use this model to guide timely support, including digital cognitive behavioural therapy, psychoeducation, and lifestyle coaching.
The outcome will be an evidence-based early warning system that enables personalised mental health care during this critical transition. This project’s significance lies in its innovative use of publicly available data to improve early detection and intervention, ultimately enhancing the well-being and quality of life for women navigating menopausal hormonal changes.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Dr Tasneem Rahman and Prof. Alistair McEwan
Eligibility: Students with any given experience around the field of research of neuroscience and neuroimaging, computational simulation and modelling, algorithm development will be given priority.
Project Description:
The project aims to develop a data-driven simulation model to predict and enhance cognitive performance and resilience in astronauts during high-risk space missions. By utilising open-access datasets such as NASA’s Cognition Battery, MAUS, and MOCAS, the project expects to create computational tools that simulate changes in attention, memory, and decision-making under varying stress and workload conditions. The project expects to identify key physiological and behavioural markers linked to cognitive decline and recovery without requiring new data collection. The outcome will be a validated simulation framework capable of forecasting cognitive performance trajectories, enabling timely intervention strategies during long-duration missions.
This project’s significance lies in improving astronaut safety, mission success, and psychological well-being by providing a scalable, cost-effective approach to cognitive monitoring and support. Additionally, the model has potential applications in other high-stress domains such as military operations and remote healthcare.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisor: Dr Daria Anderson
Eligibility:
Project Description:
Invasive neuromodulation is used as a last-line therapy for drug-resistant epilepsy. The Anderson group investigates new neurostimulation approaches in a mouse model of temporal lobe epilepsy, using long-term 24/7 Video/EEG monitoring to determine parameters that drive seizure reduction.
To track improvements in memory and cognitive deficits associated with epilepsy, this project will run behaviour tests including the novel object recognition task, the Barnes maze task, and contextual fear conditioning. These tasks allow us to examine whether neurostimulation not only alleviates seizures but also improves memory and cognitive outcomes.
This project has a substantial hands-on component with mice. Onboarding for animal research typically requires 1-2 months to complete ethics approvals, modules, and hands-on training. Students must complete the ComPass Phase 1 (Online) and ITAR module (On Oct 12) before the summer intensive research session so they can be added to the ethics protocol prior to project start.
Requirement to be on campus: *Yes, dependent on government’s health advice
Supervisors: Dr Daniele Vigolo, Prof. Omid Kavehei, Prof. Ken-Tye Yong
Eligibility:
Project Description:
Point-of-care (POC) devices are compact diagnostic platforms designed to perform rapid medical tests at the general practitioner’s office, at home, or in remote communities without requiring centralised laboratories. They hold major potential for improving access to healthcare by enabling timely blood analyses at the patient’s side.
This project will involve developing computational fluid dynamics (CFD) simulations to optimise fluid flow in a novel microfluidic device designed for blood manipulation. Our team has created a prototype single-channel system, and the next step is to scale this up using a parallelised channel design. A key challenge is ensuring balanced flow distribution across multiple channels while accounting for pressure drops and geometric constraints. The student will investigate channel configurations, assess performance under different flow conditions, and propose optimised designs. This research will contribute to advancing scalable POC devices and provide valuable training in computational modelling, biomedical engineering, and translational healthcare technologies.
Requirement to be on campus: No
Supervisors: Michael Wong, Prof. Alistair McEwan
Eligibility: Applicants should be enrolled in biomedical engineering, electrical engineering, medical science, or related fields. Prior laboratory experience is desirable but not essential.
Project Description:
This project will explore the development of a real-time hormone biosensor platform designed to advance women’s health monitoring.
Working at the interface of biomedical engineering and clinical research, the project will focus on detecting key reproductive hormones using electrochemical sensing technologies.
Potential applications include fertility tracking, contraception management, and pre-menstrual care, providing women with personalised, continuous data to better understand and manage their health.
Students will contribute to sensor fabrication, surface chemistry optimisation, and preliminary validation studies in collaboration with Westmead Clinical School.
The project offers exposure to both laboratory techniques and clinical translation pathways, with the broader aim of supporting novel diagnostic tools that empower women with accessible, real-time insights into their hormonal cycles.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisors: Dr Ann-Na Cho, Summer Cao
Eligibility:
Project Description:
The recent breakthrough in developing the Brain organoid which involves cutting-edge technology of human stem cells and knowledge of nervous system development has been highlighted as a next-generation in vitro model for personalised disease modeling and medicine. The 3 dimensional (3D) self-organising Brain organoid created from iPSCs enables the recapitulation of the endogenous cellular composition, organ-specific structure, phenotype, and functionality of human physiology and development including embryogenesis, aging. Furthermore, to reflect the complexity of the central nervous system, organoid methodologies have ever advanced to physically assemble two or three different brain regions of organoid to be integrated into one organoid called ‘assembloid’.
Our novel organ-on-chip platform will not only enable an investigation of neuronal crosstalk between diverse brain organoids and microenvironment but also construct the psychiatric disorder model for personalised therapeutics. This project will suit students interested in stem cell engineering, organ-on-chip and knowledge of neuroscience, developmental biology, drug development.
Requirement to be on campus: *Yes, dependent on government’s health advice.
Supervisor: Dr Jinglei Lv
Eligibility:
Alzheimer’s disease (AD) is highly heterogeneous, with patients showing diverse trajectories of cognitive decline, pathology, and treatment response. Current clinical tools fail to account for this neurobiological diversity, leading to inconsistent diagnoses and limited therapeutic success.
This project will apply state-of-the-art neuroimaging methodologies—including multimodal MRI (structural, diffusion, and functional imaging), lesion mapping, and connectomics—combined with machine learning approaches such as normative modelling and digital twin frameworks. By integrating large-scale imaging datasets (e.g., UK Biobank, ADNI, AIBL) with clinical and cognitive data, we aim to map distinct subtypes of AD, uncover early biomarkers, and predict disease progression on an individual level.
The ultimate goal is to translate these findings into clinically actionable tools that can guide precision therapies and improve patient outcomes.
Requirement to be on campus: No
Supervisor: Dr Jinglei Lv
Eligibility:
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.
Requirment to be on campus: No
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Last updated 23rd September 2025.
