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Full time fixed term 2-year position Work within a major industry‑backed, multidisciplinary research partnership developing cutting‑edge metal composite coatings for extreme industrial environments Base Salary $113400 + 17% superannuation About the opportunity We are seeking a motivated...
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DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site of transcription factors or RNA-binding proteins. DNA or RNA binding sites are often variable. However,...
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This research focuses on developing and evaluating methodologies for the optimal design of control charts within the framework of Statistical Process Control (SPC). The study aims to determine the
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In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference
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research often overlooks the complexities of mixed-vehicle environments, and the development of optimal deployment, routing, and charging strategies. This project aims to address these gaps by optimising
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management of laboratory animals, ensuring optimal welfare outcomes while supporting researchers with accurate data and expert technical services. This is a rewarding opportunity to contribute to meaningful
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automated recovery algorithms, improving system resilience. Research Areas for Master’s and PhD Students AI-Enhanced Resource Forecasting and Optimization: Research Focus: Developing and testing ML algorithms
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approaches. Apply hybrid optimisation techniques (e.g., quantum-inspired or QAOA-based methods) to determine optimal intervention strategies under resource constraints. Compare the performance, scalability
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compressed into lightweight student models using knowledge distillation, enabling efficient real-time inference on mobile devices. The distilled models will be deployed and optimized on mobile platforms, with
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determining the appropriate design pattern for a specific scenario, identifying relevant quality attributes for a particular design choice, and recognizing the optimal timing for implementing a refactoring