669 systems-science-"https:"-"https:"-"https:"-"https:"-"Hasselt-University" positions at Monash University
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postgraduate qualification in Data Science / Computer Science (PhD preferred) Strong expertise in Python and/or R, SQL, data engineering and machine learning Experience with EMR systems (Cerner highly desirable
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rounds per year Total scholarship value $37,145 p.a (2026 rate) Number offered Varies See details Bachelor of Biomedical Science Ilaria My scholarship has given me immense support financially, especially
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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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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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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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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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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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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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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