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development programs, including Indigenous cultural networking opportunities, mentoring and career development Be part of a modern, global, research-intensive University, with an enduring commitment
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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
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others to support a well-rounded team. In return, you will be part of a team that values innovation, applying cutting edge solutions to complex problems, allowing you the freedom to solve important
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components of living cells, including the genetic material, in order to understand biological processes and how these are altered in disease. Research and teaching in the department encompasses six broad
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Level 8 (plus 17% employer superannuation) Utilise your strong technical aptitude across a complex multi-university structure. Process complex information from multiple data sources Be part of a team that
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Doctorate and Research Masters degrees at Australian universities. As part of this program, a RTP Fees Offset is available to cover the course tuition fees whilst studying at a Monash Australia campus
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Applicants must be: a commencing or continuing full-time internal/on-campus PhD student (or a part-time PhD student with a medical condition or significant carer commitments) based at a Monash campus in
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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totalling approximately $47,000 per annum tax exempt (2025 rate). A four year Project Expense and Development package of $13,000 per annum. A 60 day Industry Engagement component with the industry partner. A