Connecting Spatial and Spectral Information: Understanding Complex Materials Systems at the Molecular Level with Machine Learning - PhD Scholarship
Scholarship code: SRS-25030
These prestigious La Trobe University scholarships (2), in partnership with the Australian Office of National Intelligence, will be awarded to outstanding applicants interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning.
PhD Student A will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models to individualise tumour type, associated biomarkers and genomic characteristics to high precision. The resulting multipurpose machine learning workflows will lead to rapid precision diagnoses and effective individualized cancer care plans. Coding and user interface development skills will be developed.
PhD Student B will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for future autonomous instrument control and self-directed experimentation will be developed, recognizing the challenge presented by the integration of multiple complex systems. Coding and user interface development skills will be developed.
This scholarship is open only to Australian citizens, Australian permanent residents, or New Zealand special category visa holders. The Expression of Interest will remain open until the positions are filled.
Benefits of the scholarship
- a stipend scholarship for three and a half years, with a value of $40,000 per annum (pro-rata), to support your living costs
- a fee-relief scholarship for up to four years
- opportunities to work with La Trobe’s outstanding researchers in state of the art laboratories, and have access to our suite of professional development programs
- collaborative projects, including time spent with the Olivia Newton-John Cancer Research Institute and CSIRO
- opportunities to travel to conferences in Australia and overseas
In selecting successful applicants, we prioritise applications from candidates who:
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