Advanced Machine Learning in Cancer Research
1 week ago
This exciting research opportunity is a prestigious scholarship in partnership with the Australian Office of National Intelligence. The successful applicant will work at La Trobe University's Melbourne campus, developing innovative machine learning workflows to understand complex materials systems at the molecular level.
- The PhD Student will be based at La Trobe Melbourne Campus and will 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.
This scholarship is open only to Australian citizens, Australian permanent residents, or New Zealand special category visa holders.
Benefits include:
- a stipend scholarship for three and a half years to support your living costs.
- a fee-relief scholarship for up to four years.
- opportunities to work with outstanding researchers in state-of-the-art laboratories and have access to professional development programs.
- collaborative project opportunities, including time spent with leading research institutions.
- opportunities to travel to conferences in Australia and overseas.
Eligibility criteria:
To be eligible to apply for this scholarship, applicants must:
- be Australian citizens, Australian permanent residents, or New Zealand special category visa holders.
- agree to be enrolled full-time and undertake their research at the La Trobe University Melbourne (Bundoora) campus.
In selecting successful applicants, we prioritise applications from candidates who:
- have an outstanding record of prior performance.
- have completed a Masters by Research or other significant body of research, assessed at a La Trobe Masters by research standard of 75 or above.
- have outstanding grounding in one or more relevant disciplines including cancer biology, engineering, machine learning / data science, coding.
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