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Mixed-Integer Programming (MIP) solvers are very powerful tools to solve combinatorial problems that arise in many industries. Modern MIP solvers usually run a sequence of algorithms to solve
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formative assessment and personalised feedback while ensuring fairness, accountability, and transparency. The research will explore a combination of algorithmic design, human–AI interaction, and empirical
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Research Training Program (RTP) Fees-Offset Research Training Program (RTP) Scholarships, funded by the Australian Government, support both domestic and international students undertaking Research
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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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algorithms for computing MML solutions beyond the one-dimensional case. Extend existing dynamic programming approaches to higher-dimensional problems or develop novel approximation methods that preserve
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cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science. The purpose of this project is to use game theory and computational
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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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of policy conditions. The fellowship project is based on critical theories from fields like political economy and STS, alongside qualitative methods including document analysis and ethnography. Proposals
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Computational drug discovery Primary supervisor Geoff Webb Research area Data Science and Artificial