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Build and maintain strong relationships across internal and external networks What You’ll Bring A postgraduate qualification in a relevant field or extensive healthcare experience Proven expertise in
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PhD Scholarship – Modelling the social and political drivers of net zero transitions Job No.: 670767 Location: Clayton campus Employment Type: Full-time Duration: 3.5-year fixed-term appointment
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% employer superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative community Identified role. Open to Indigenous applicants only Only Indigenous Australians
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PhD Scholarship in Digital Mapping of Homemade & DIY Cultural Economies in First Nations Communities
PhD Scholarship in Digital Mapping of Homemade & DIY Cultural Economies in First Nations Communities Job No.: 681214 Location: Clayton campus Employment Type: Full-time Duration: 3.5-year fixed-term
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% employer superannuation) Prestigious purpose-led organisation Shape global reputation Newly created position The Opportunity Reporting to the Executive Director, Communications, the Senior Strategic Adviser
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optimisation framework to align the national HSR network with theoretical city sizes and sustainability outcomes. Cross-National Theory Testing Evaluate the generalisability of the sustainable city size
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Level 07 (plus 17% employer superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative community Be surrounded by extraordinary ideas - and the people who discover
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, collaborative community Be surrounded by extraordinary ideas - and the people who discover them The Opportunity Monash University is seeking a Senior Infrastructure Engineer to lead the design, deployment, and
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such systems are limited to the learning errors due to the neural component. In this Ph.D. project, you will be exploring the use of Lipschitz Continuous Neural Networks to learn Lipschitz-bounded neural models
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such systems are limited to the learning errors due to the neural component. In this Ph.D. project, you will be exploring the use of Physics-Informed Neural Networks to encode the symbolic knowledge