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or nats, balances model complexity against goodness-of-fit. It is essentially a formal implementation of Occam's razor. A key advantage of MML is that the message length provides a universal gauge for
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, mainly in the area of search-based software testing to verify that the AI components of self-driving cars work as they should. This project is in collaboration with Professor Hai Vu and the Monash
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This project is technical in nature and would suit a candidate with a background and interest in #Java programming, health informatics or health data (or a combination thereof). The primary aim of this work is the extend and localise (to the Australian context) the open source Synthea stack....
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Testing AI/LLM systems Primary supervisor Yongqiang Tian Research area Software Engineering In
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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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the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency. This project aims to deliver a smart software vulnerability
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Interoperability (using FHIR) in cutting-edge medical software systems Primary supervisor Chris Bain
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Motivation: In today’s increasingly digitalised world, software defects are enormously expensive. In 2018, the Consortium for IT Software Quality reported that software defects cost the global
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. The project aims to examine how these credentials can be designed and governed to ensure authenticity and trust while safeguarding learners’ privacy. By combining technical analysis, formal modeling, and policy
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and embed self-awareness modules within these systems. This will likely involve a combination of the standard methods in these systems, such as transformer or diffusion networks and reinforcement