This opportunity is designed for outstanding individuals interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning.
The successful candidate 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 individualised cancer care plans. Coding and user interface development skills will be developed.
A key component of this role is collaboration with La Trobe's outstanding researchers in state-of-the-art laboratories and access to our suite of professional development programs. There are also opportunities to travel to conferences in Australia and overseas.
To be eligible for this opportunity, applicants must be Australian citizens, Australian permanent residents or New Zealand special category visa holders. They should also be willing to be enrolled full-time and undertake their research at the La Trobe University Melbourne (Bundoora) campus.
Selection will prioritise applications from candidates who have an outstanding record of prior performance, a strong grounding in relevant disciplines including cancer biology, physics, chemistry, engineering, machine learning/data science and coding, and experience in developing machine learning workflows.
* Candidates will be required to submit a statement outlining their motivation and suitability for this opportunity, as well as their specific skills and experience relevant to the PhD project.
* A WWCC will also be required prior to commencing the position.
Shortlisted applicants will be invited to attend an interview and may be asked to participate in further evaluation activities.