AI PhD Scholarship - Opportunity: Foundation Models for Brain Data 

Job No.: 696618

Location: Clayton campus

Employment Type: Full-time

Duration: The scholarship may be held for up to 3.5 years (fulltime) for Research Doctorate (PhD) studies

Remuneration: The successful applicant will receive

• A Research Living Allowance, at current value of $37,145 AUD per annum for PhD (2026 rate with annual indexation)
• Faculty of Information Technology Tuition Fee Scholarship (for international students only)
• Top-up scholarship of $10,000 AUD per annum
• FIT Candidature Funding of $4,000 AUD for the duration of the candidature
• Up to $1,265 AUD from Monash Graduate Research Office as a one-off travel grant
• Top-up government scholarship $7,135 AUD per annum
• Travel support of $2,000 AUD per year to attend and present research at leading international conferences.

Indicative total stipend: approximately $54,280+ per annum (tax-free), plus up to $13,265 in travel support and access toEmotiv neurotechnology, computing and AI infrastructure.

The Opportunity

This is an unprecedented opportunity for an outstanding data science and AI PhD candidate interested in brain data analysis and AI, supervised by Dr Mahsa Salehi at Monash University. The successful candidate will join our world-leading team in Temporal Analytics Lab, a world leading research group uniquely combining research in time series forecasting, classification, segmentation, anomaly detection and learning in the context of non-stationary distributions. The candidate will also be connected to the Faculty of Information Technology.

The Temporal Analytics Lab, directed by Dr Mahsa Salehi, is home to a range of innovative tools and projects – including the only current AI-focused Australian Laureate initiative led by world-renowned expert Distinguished Professor Geoff Webb, developing AI systems that can understand a continuously changing world. The Temporal Analytics Lab focuses on understanding patterns, making predictions and detecting unusual behaviour in data over time – driving outcomes such as proactive healthcare, reliable disaster predictions and thriving societies.

Emotiv, a global neurotechnology company, will provide $10,000 p.a. stipend top-up and $2,000 p.a. travel support, plus access to $50,000 worth of neurotechnology facilities, computing resources, and AI infrastructure.

To be considered for this opportunity you should fulfil the eligibility requirements listed below. The academic qualification requirements for this PhD is:

• A bachelor’s degree of at least four years in a relevant discipline, which includes a research thesis or project, with a minimum overall average grade of an honours degree equivalent to the First Class Honours; or
• A master's degree in a relevant discipline which includes a research thesis or project equivalent to at least 25 percent of one year of full-time study, with a minimum overall average grade of honours equivalent to the First Class Honours; or
• A qualification, or combination of qualifications and relevant professional experience, deemed equivalent by the GRC (or delegate).

The ideal PhD candidate will have:

• A strong background in machine learning, deep learning, and signal processing
• Proficiency in Python and machine learning frameworks such as PyTorch (required)
• Knowledge of neural networks, transformers, and representation learning techniques (desirable)
• Experience in time-series modelling, biosignal analysis, or neuroscience (advantageous but not essential)
• Strong analytical skills, curiosity for interdisciplinary research, and the ability to collaborate effectively with both academic and industry teams

Monash University strongly advocates diversity, equality, fairness and openness. We fully support the gender equity principles of the Athena SWAN Charter.

The Project

We invite applications from outstanding PhD candidates with an undergraduate or postgraduate qualification in a computing discipline such as data science, artificial intelligence, machine learning or computer science.

which has included training in qualitative research.

This project aims to develop a scalable EEG foundation model capable of learning general-purpose representations of brain activity that can support diverse neurotechnology and multimodal AI applications. Specifically, the project objectives are as follows:

1. To develop a general-purpose EEG foundation model that learns unified representations of  brain activity from large and diverse EEG datasets with different configurations (e.g., number of channels, sampling frequency and resolution).
2. To leverage large-scale self-supervised learning to train models on unlabeled EEG data, reducing reliance on expensive manual annotations.
3. To improve cross-device and cross-task generalisation, enabling models trained on one dataset or device to adapt to others with minimal retraining.
4. To improve detection and monitoring of medical conditions by leveraging new or existing medical datasets
5. To support multimodal brain–AI applications, exploring how EEG representations can interact with language and visual models for tasks such as EEG-to-text decoding and EEG-guided generative systems.

This project is expected to develop a novel EEG foundation model capable of learning unified representations of brain activity from heterogeneous datasets. The research will leverage large-scale EEG recordings collected from multiple devices and experimental paradigms through collaboration with Emotiv, a global neurotechnology company.

The resulting model will support applications such as cognitive state estimation, brain–computer interfaces, and mental wellbeing monitoring. The learned representations will also enable integration with language and vision models, supporting emerging capabilities including EEG-to-text decoding and AI-driven neurotechnology applications.

This position has a two-stage selection process:

Stage 1: Please submit mahsa.salehi@monash.edu. 

With the EOI please include the documents - CV, academic transcripts, a cover letter and a draft research proposal of up to 2 pages, responding to one or more of the above research objectives.

The draft research proposal should outline your interest in being a PhD candidate within the Temporal Analytics Lab and summarise the theoretical and methodological approaches you are interested in pursuing, such as self-supervised learning, representation learning, transformers, and multimodal (brain–language and brain–vision) modelling.

Stage 2: Candidates who pass this stage of the selection process will be invited to discuss their ideas before developing and submitting a full application.

Enquiries: Dr Mahsa Salehi, mahsa.salehi@monash.edu

Applications Close: Sunday 30 August 2026, 11:55pm AEST

We will begin the interview process as soon as suitable applications are received, so applicants are encouraged to apply early. We will not wait until the closing date to start shortlisting and interviews.