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Automated Program Repair (APR) is the grand challenge in software engineering research. Many APR methods have shown promising results in fixing bugs with minimal, or even no human intervention
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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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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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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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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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systems. You will contribute to the design, testing, and deployment of cutting-edge control, navigation, and autonomy stacks running on onboard computers across ROS/ROS2-based platforms. Key
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School of Electrical Engineering and Computer Science Full time (100%), fixed-term position for up to 12months Base salary will be in the range $82,057.75 - $109,246.18+ 17% Superannuation (Academic
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, Medicine, or Engineering) demonstrated experience or knowledge of one or more of the following: computational algorithm development working with medical images, in particular CT or cone-beam CT a
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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