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Project Scholarships (available year-round until filled), or the other scholarships available in the mid-year scholarship round . Benefits of the scholarship a Research Training Program (RTP) or La Trobe
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La Trobe University is strongly committed to providing employment opportunities to Aboriginal and Torres Strait Islander peoples in all academic and professional roles and levels. Indigenous Australian candidates can register an Expression of Interest for consideration and notification of roles...
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Background La Trobe's Regional Benefits Program empowers high achieving students from regional communities to live, study and thrive in regional Victoria. This program is available to students
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in program/event management and development, and ability to manage multiple projects concurrently. Demonstrated commitment to social justice. Please scroll down to apply. Aboriginal and Torres Strait
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employee benefits Lead the development of a strong innovation and commercialisation pipeline to maximise research impact and industry income across multiple markets About the position La Trobe University is
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resources and researchers who work with industry and Government to address the sustainability and liveability issues facing global communities. PhD students in our Joint Doctoral Degree Program will have
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Australian or New Zealand citizens or Australian permanent residents newly enrolling in a PhD. Students undertaking the joint PhD program will be enrolled in a PhD at both institutions. Your supervisory team
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Australian or New Zealand citizens or Australian permanent residents. Students undertaking the joint PhD program will be enrolled in a PhD at both institutions. Your supervisory team will comprise of academic
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or the placement and the student program will be provided prior to the commencement of placement. Clinicians will be required to attend an aged care setting weekly on the specified clinic day/s and support students
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models