683 systems-science-"https:"-"https:"-"https:"-"https:"-"IRIBHM-ULB" positions at Monash University
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in clinical trials and may be a clinician, researcher, or student who is affiliated with an APT. The project will involve using an organisational readiness lens to understand and implement a co
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are inherently interpretable for people, MDPs with thousands of states are difficult to understand by humans. The K-MDP problem is the problem of finding the best MDP with, at most, K states by leveraging state
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be more challenging when the focus is minority groups. These scholarships will assist a student who is from a minority or who completes research focussing on a minority group. Total scholarship value
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June Howqua Grant Achieving Potential Support Scholarship This scholarship is available to high achieving Monash students studying medicine who are experiencing financial hardship. Total scholarship
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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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area Software Engineering The objective of this project is to design automated approach to detect bugs in various software, e.g., compilers, data libraries and so on. The project may involve LLMs
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-oriented, proactive and passionate about delivering impactful administrative services, this is your opportunity to make a difference. About Monash ARTS Monash Arts is one of Australia’s largest and most
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comparing model predictions of observed transient data Experience in software development with larger scientific codes; in particular, expertise in Fortran, C, Python, and/or build systems are of advantage As
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of the following would be useful: stakeholder engagement, (IT) system design, basic web programming, and technology evaluation. Experience in Indigenous and qualitative research methods is desirable, and Indigenous
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Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is