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models (LLMs) have significantly improved predictive performance, most existing AI systems remain passive prediction tools that lack transparency, reasoning capability, and reliability. These limitations
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at Monash University. The successful candidate will join our world-leading team in Temporal Analytics Lab, a world leading research group uniquely combining research in time series forecasting, classification
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๐๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ฒ at Monash University and work closely with ๐๐ฅ๐ฎ๐ซ๐๐ฅ๐ข๐ฌ ๐๐๐ฌ๐๐๐ซ๐๐ก, the industry partner on this project. The project focuses on developing ๐๐ง๐๐ซ๐ ๐ฒ
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hardware-aware AI design, this project explores scalable approaches to improve perception accuracy, system reliability, and responsiveness in dynamic environments. Researchers and students will investigate
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, and reliable multimodal intelligence directly on edge devices. Researchers and students will investigate novel approaches to improve inference efficiency and adaptability, preparing them for cutting
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the Faculties of Information Technology and Arts at Monash University, the largest university in Australia which regularly ranks in the top 50 universities worldwide, and housed within the Monash AI Institute
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centreโ, โschoolโ, โshopping centreโ, โhotelโ, โbus stopโ, โtrain stationโ, and so on, the system provides a reliable way to identify key landmarks in urban and suburban environments. Using advanced
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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining
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The outcomes will contribute to next-generation data systems that leverage quantum acceleration where beneficial, while maintaining practical deployability and system reliability.
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potential to facilitate the process of generating health-related advice without the need for predefined rules or training data. Yet, their reliability remains a serious concern. This project aims to first