Master of Research
The Master of Research at Western Sydney University is a research training degree that provides you with a pathway to PhD study and a research career.
The Master of Research is an internationally recognised postgraduate qualification that will help you to pursue opportunities in academia, government, non-government organisations, research institutes and international organisations.
The first year of the degree involves an intensive research training coursework program where you will engage with peers and academics in an interdisciplinary environment. The coursework is designed to help you build your skills as a research, master the art of knowledge translation and develop a strong research proposal.
In the second year of the degree, you will take on a research project under the supervision of a specialist in your field to produce a 20,000 - 25,000 word research thesis.
For domestic students, the coursework year attracts Commonwealth Supported Places (CSP) and the second year offers a place via the Research Training Scheme (RTS), which does not attract any tuition fees.
The Master of Research degree is completed in 2 years full-time, or part-time equivalent.
Applications are accepted throughout the year with 1H (first-half) and 2H (second-half) intakes available.
For more information and to apply, visit the Master of Research website (opens in a new window)
Thinking of commencing study in 2027?
Below are just some of the projects on offer with us. Review the Master of Research course and eligibility information, then contact the nominated supervisor below with a brief outline of your background, research interests and the skills you would like to develop.
Language & Cognition
Learning New Speech Sounds: Personalising Spoken-Language Training
Why do some people learn unfamiliar speech sounds more easily than others? This project examines how training design, corrective feedback and individual cognitive abilities shape spoken-language learning, with the goal of developing more personalised and effective approaches to language training.
Research areas: Psychology; linguistics; speech perception; language learning
What the student may do: Design and run a spoken-language learning experiment; Measure learning and individual-difference factors; Analyse behavioural data and interpret patterns of learning.
Ideal Background: Interest in language, cognition or learning; Willingness to develop experimental-design and statistical-analysis skills; Cognitive neuroscience knowledge is helpful but not essential.
Capabilities Devloped: Experimental design, behavioural research, quantitative analysis and interdisciplinary language-science experience.
Supervisors: Mark Antoniou, Hannah Sarvasy
NeuroAI & Brain Science
This project compares the layered representations created by modern AI vision models with time-resolved human brain activity. Using computational modelling and EEG, the student will investigate which model architectures and representational features best explain the dynamics of human visual processing.
Research areas: Cognitive neuroscience; artificial intelligence; NeuroAI; visual cognition
What the student may do: Extract and compare representations from modern AI vision models; Analyse time-resolved EEG data; Link computational predictions to human neural signals.
Ideal Background: Python programming and statistics are required; An interest in AI, psychology or brain science; EEG or NeuroAI experience is advantageous but not essential.
Capabilities Devloped: Neuroimaging analysis, machine-learning workflows, representational modelling and reproducible computational research.
Primary Supervisor: Tijl Grootswagers
Mouse movements contain a continuous trace of an emerging decision. This project links movement trajectories with AI-model representations and neural data to test whether simple behavioural measures can reveal the time course of perception, representation and choice.
Research Areas: Cognitive and computational neuroscience; NeuroAI; behavioural modelling
What the student may do: Analyse mouse-tracking trajectories as time-resolved behavioural data; Generate and compare model-based representational spaces; Relate behavioural representations to EEG, MEG or fMRI evidence.
Ideal Background: Python programming and statistics are required; Interest in behavioural experiments and computational modelling; Experience with mouse tracking, neuroimaging or representational similarity analysis is advantageous.
Capabilities Developed: Time-series analysis, behavioural modelling, representational similarity analysis and model–brain comparison.
Primary Supervisor: Tijl Grootswagers
How does the brain respond to art made by generative AI? Combining behavioural or EEG data with representations extracted from generative models, this project examines which computational features align with—or diverge from—human perception and aesthetic experience.
Research Areas: Cognitive neuroscience; neuroaesthetics; generative AI; human–AI interaction
What the student may do: Develop an experiment using AI-generated artworks; Collect behavioural data or EEG, depending on project scope; Compare human responses with model-derived representational spaces.
Ideal Background: Python and statistics are required; Interest in art perception, cognition or generative AI; EEG, signal-analysis or experimental-aesthetics experience is advantageous.
Capabilities Developed: Experimental neuroaesthetics, generative-model analysis, EEG or behavioural methods and interdisciplinary communication.
Primary Supervisor: Tijl Grootswagers
This project asks what makes an artificial vision system brain-like. By comparing feedforward, recurrent and branched neural networks with time-resolved EEG representations, the student will identify which architectural and training choices best capture
Research Areas: NeuroAI; computational neuroscience; machine learning; visual cognition
What the student may do: Implement or evaluate multiple neural-network architectures.; Derive representational spaces across layers and time.; Compare artificial representations with human EEG dynamics.
Ideal Background: Python, statistics and neural-network experience are required; Interest in visual cognition and computational neuroscience; RSA, CCA or neuroimaging experience is advantageous.
Capabilities Developed: Machine learning, neural geometry, EEG analysis and model–brain comparison.
Primary Supervisor: Tijl Grootswagers
AI-generated faces can appear strikingly realistic, yet behavioural judgements and brain responses may tell different stories. This project combines computational face representations with time-resolved EEG to identify the features that support—or disrupt—the neural discrimination of real and synthetic faces.
Research Areas: Cognitive neuroscience; NeuroAI; face perception; generative modelling
What the student may do: Curate or work with real and synthetic face stimuli; Analyse time-resolved EEG and behavioural judgements; Compare neural patterns with computational face embeddings.
Ideal Background: Python and statistics are required; EEG-analysis experience or a strong willingness to learn; Knowledge of generative models, face recognition or RSA is advantageous.
Capabilities Developed: EEG decoding, computational modelling, face-perception research and critical analysis of generative AI.
Primary Supervisor: Tijl Grootswagers
Space Communication & Bio-Inspired Sensing
Laser communication could transform data transfer between Earth and satellites, but atmospheric turbulence distorts the signal. This project develops advanced wavefront sensors using event-based cameras, progressing from optical simulation and laboratory design to field testing at Mount Stromlo Observatory.
Research Areas: Optical engineering; event-based sensing; signal processing; space communication
What the student may do: Model atmospheric and optical effects; Develop and test event-based wavefront-sensing approaches; Contribute to laboratory and field experiments.
Ideal Background: Strength in one or more of mathematics, physics, programming or practical experimentation; Willingness to collaborate across complementary skill sets; Interest in optical systems, sensors or space technology.
Capabilities Developed: Optical design, signal processing, event-based sensing and field testing.
Supervisors: Nimrod Kruger, Gregory Cohen
Event cameras record change with microsecond timing rather than capturing repeated full frames. This project applies neuromorphic sensing to optical neural recording, developing microscopy methods that preserve fast activity while reducing data volume across long experiments.
Research Areas: Optical imaging; microscopy; neuroscience; neuromorphic sensing
What the student may do: Develop and test an event-based microscopy system; Characterise temporal performance and data efficiency; Work across engineering and neuroscience research settings.
Ideal Background: Physics experimentation and basic programming are required; Interest in optical instrumentation and neuroscience; No biological-tissue handling is expected from the MRes student.
Capabilities Developed: Optical instrumentation, neuromorphic imaging, experimental characterisation and interdisciplinary teamwork.
Supervisors: Nimrod Kruger, Yossi Buskila
Event-based cameras promise fast, efficient imaging, but the field lacks consistent measures for comparing complete systems. Through iterative design, prototyping and testing, this project will define meaningful performance metrics and establish evidence-based benchmarks for advanced optical sensing.
Research Areas: Data science; optical sensors; mathematics; experimental engineering
What the student may do: Define measurable system-level performance criteria; Design and prototype comparative experiments; Develop analysis pipelines and evaluate sensing performance.
Ideal Background: Physics, statistics and basic programming are required; Interest in measurement, instrumentation and data interpretation; A practical approach to iterative experimentation.
Capabilities Developed: Experimental design, sensor characterisation, benchmarking, data pipelines and technology evaluation.
Supervisors: Nimrod Kruger, Gregory Cohen
Neuromorphic systems process information as spikes, but efficient and standardised interfaces remain an open challenge. This project combines mathematical primitives, spiking operators and photonic computing to investigate robust ways of translating spike-based information between sensors, processors and digital systems.
Research Areas: Neuromorphic computing; artificial intelligence; photonics; physics; mathematics
What the student may do: Develop mathematical representations for spike-based information; Explore spike-to-spike and spike-to-digital interfaces; Test selected concepts through simulation or laboratory demonstration.
Ideal Background: Mathematics, physics and basic programming are required; Interest in neuromorphic or photonic computing; Comfort moving between theoretical and experimental questions.
Capabilities Developed: Mathematical modelling, spiking systems, photonic-computing concepts and research across theory and implementation.
Supervisors: Nimrod Kruger, Paul Hurley