We are seeking Expressions of Interest from potential Graduate Researchers to work on the following PhD projects. If interested, please complete the Expression of Interest form at:

Description of the project environment:

These projects are embedded within the Centre, where up to 14 PhD students, 5 post-doctoral researchers, and 11 University of Melbourne staff will collaborate across 4 research streams with researchers at IBM Research and a number of other Australian Universities, including RMIT University, the University of South Australia, and Western Sydney University. Each student will be jointly supervised by Universities and IBM Research and will spend at least 12 months of their candidature working on site at an IBM Research laboratory.

Financial assistance:

These positions are supported by a tuition waiver and a supplemented living stipend of A$34,828 per annum.

Using modern Machine Learning for probabilistic modelling of dementia

The central goal of this project is to develop novel predictive tools that use multi-modal data (demographic, clinical, imaging and genetics) to statistically model age of onset of Alzheimer’s disease (diagnosis) as well as its subsequent trajectory (prognosis). These tasks come with a non-standard set of challenges (e.g. large chunks of missing data, dataset shifts across cohorts, frequent mislabelling, undocumented comorbidities and sparsity of observations compared to the disease timescale), thus representing a relatively uncharted territory for modern AI. On the quest to find the right tool for the job, you are likely to explore the entire spectrum of Machine Learning: from SVMs to Gaussian Processes, from PCA to Variational Autoencoders, from logistic regression to GBMs, from linear models to Deep Learning and so on.

The suitable candidate will have a BSc (Hons) or MSc degree in Computer Science, Statistics, Mathematics, Machine Learning or related fields, with a solid foundation in statistical inference and machine learning, extensive experience with R or Python. Keen interest in applying modern ML tools to the study of cognitive processes is essential.

Key areas involved in the research: machine learning, Bayesian data analysis, mixed modelling, cognitive decline

Using deep learning to improve genomic risk prediction of Alzheimer’s disease

It’s well known that an individual’s genetic profile affects their risk of Alzheimer’s disease and their rate of cognitive decline. This project aims to understand how deep learning may be applied to a wealth of existing genetics-related data in order to improve prediction of an individual’s risk of Alzheimer’s disease. The focus will be on models and architectures that allow for the integration of other modalities (e.g imaging, cognitive tests). We will also explore how the resulting prediction relate to other established Alzheimer’s biomarkers.

The suitable candidate will have a BSc (Hons) or MSc degree in Bioinformatics, Computer Science, or related fields, with a solid foundation in machine learning, applied statistics, programming, and data analysis. The ideal candidate will have experience working with human genomics data and/or deep learning, a deep understanding of statistical inference and machine learning, and extensive experience with R or Python.

Key areas involved in the research: machine learning, genomic risk prediction, cognitive decline

Context-specific Clinical Decision Support Leveraging Clinical Practice Guidelines

This PhD project will focus on identification of relevant clinical guidelines for a given clinical situation. In particular, the emphasis of the project will be to explore methods from information retrieval and natural language processing to identify key clinical practice guidelines to guide clinical decision making. It will require some modelling of the typical clinical decision-making process and understanding of key decision points in clinical care, in order to align clinical situations to particular steps or stages in a clinical guideline. It will explore the required depth of analysis of guidelines relative to the clinical situation – is it sufficient to retrieve a complete guideline, or do specific paragraphs or sentences need to be identified? How important is presentation of the background evidence in the published literature to support the clinical decision?

If suitable clinical data sets can be identified, the project will also consider how to use clinical data from medical consultations to trigger identification of a relevant clinical guideline at a relevant moment in a patient interaction, to support clinical decision making.

The suitable candidate will have a BSc (Hons) or MSc degree in Computer Science, Computational Linguistics, Artificial Intelligence, or related fields, with good coursework results, evidence of strong capacity for research, and good English skills.

This project will be based at RMIT University, working directly with Professor Lawrence Cavedon and will also benefit from the context of the Natural Language Processing group at the University of Melbourne, including Professor Tim Baldwin, Professor Karin Verspoor, and Associate Professor Trevor Cohn, and their existing students and research fellows. Professor Baldwin is the Director of the training centre, while Professor Verspoor is the Deputy Director and Lead of the Natural Language Processing Stream in the centre.

Key areas involved in the research: natural language processing, information retrieval, health informatics

Multi-document summarisation supporting clinical evidence review

This project will focus on summarisation of the background literature relevant to a given clinical question. Under the assumption that a relevant body of literature for a clinical question can be identified, the objective will be abstractive summarisation of that literature with sensitivity to the clinical question that is being explored.

The student project work will focus on natural language generation for the summaries, and consider adaptation of the summaries for different audiences based on readability, background knowledge, desired summary length, etc. to cater to different requirements.

This project will also benefit from the context of the Natural Language Processing group at the University of Melbourne, including Professor Tim Baldwin, Professor Karin Verspoor, and Associate Professor Trevor Cohn, and their existing students and research fellows. Professor Baldwin is the Director of the training centre, while Professor Verspoor is the Deputy Director and Lead of the Natural Language Processing Stream in the centre.

Key areas involved in the research: natural language processing, health informatics

Seizure Forecasting using Neural Networks

This project aims to improve performance of epileptic seizure forecasting. The outcome will be a system that provides estimates of the likelihood that a person will or will not have a seizure during a relevant forecasting time horizon. Artificial neural networks have demonstrated superior performance in seizure forecasting. This project will build upon our earlier work to develop new approaches based on convolutional neural networks, recurrent neural networks, and other approaches. There is potential to incorporate other sources of relevant data, such as time of day and movement tracking, as well as EEG.

The suitable candidate will have an Honours or Masters degree in Biomedical Engineering, Electrical Engineering, Mathematics, Computer Science, or related fields, with good coursework results, evidence of strong capacity for research, and good English skills.

The primary supervisor for this project will be A/Prof Mark McDonnell at the University of South Australia, but the candidate will need to spend a minimum of one year physically based in Melbourne.

Key areas involved in the research: signal processing, machine learning, neural networks

Deep Learning for Fitting Neural Mass Models to EEG (Applications for this project have now closed)

This project aims to improve performance of seizure prediction by linking neural mass models to deep learning techniques. Neural mass models have been able to model and reproduce output signals that replicate different types of intracranial EEG recordings. Data assimilation with Kalman filters has been successful in investigating how variations in model parameters can lead to the different behaviours. However, to use these models for seizure prediction, high fidelity models are needed that incorporate mechanisms of parameter variation that are intrinsic to the model. This project will develop augmented neural mass models and deep learning and other machine learning techniques to fit the models to patient-specific iEEG recordings.

The suitable candidate will have an Honours or Masters degree in Biomedical Engineering, Electrical Engineering, Mathematics, Computer Science, or related fields, with good coursework results, evidence of strong capacity for research, and good English skills.

Key areas involved in the research: advanced signal processing, machine learning, neural modelling

Brain-Computer Interfaces for low-powered mobile/wearable devices (Applications for this project have now closed)

This project aims to develop a brain-computer interface system with emphasis on low power consumption and small memory requirements, without compromising on accuracy. The outcome of this project will be novel CNN models that incorporate spatiotemporal information, produce fast inference, and achieve small memory foot-prints so that they can be deployed in wearable devices or mobile computing platforms. The greatest challenge will be to maintain the required accuracy and performance while reducing the model size and the overall latency of the system.

The suitable candidate will have an Honours or Masters degree in Biomedical Engineering, Electrical Engineering, Mathematics, Computer Science, or related fields, with good coursework results, evidence of strong capacity for research, and good English skills.

Key areas involved in the research: signal processing, machine learning, neural networks, embedded systems

Multimodal Brain-Computer Interfaces for augmented reality and virtual reality applications (Applications for this project have now closed)

This project aims to develop a brain-computer interface system that allows people with speech, vision or motor disorders to interact in an augmented/virtual environment (e.g., selecting/manipulating an object of interest, walking/exploring a remote site). This AR/VR-based BCI will allow users to learn and adapt to the BCI in a more swift manner with countless possibilities of application scenarios, compared to the conventional case of a task-specific BCI (e.g., to control a robotic arm). Furthermore, AR/VR-based BCI systems will be safer to operate, can be deployed anywhere with minimal hardware constraints, and will provide an extremely efficient training platform to decode the brain’s intent. Brain-computer interfaces rely upon good recordings of neural signals, typically with scalp or intracranial EEG systems. This project will build a multimodal BCI system that augments the EEG signals through speech recognition and gaze tracking as a multimodal input to the system to further enhance the performance/accuracy of the system for the desired application scenarios.

The suitable candidate will have an Honours or Masters degree in Biomedical Engineering, Electrical Engineering, Mathematics, Computer Science, or related fields, with good coursework results, evidence of strong capacity for research, and good English skills.

Key areas involved in the research: signal processing, machine learning, neural networks