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parents . Enquiries: Justin Robins, Group Manager Server Cloud and Compute Platforms, [email protected] Position Description: Manager - Linux Platforms Applications Close: Friday 25th September 2026
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learning, generative AI or LLM-based systems, cloud computing, and the design and validation of algorithms for high-volume quantitative or sensor-derived data. Experience with wearable data, health
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in galaxies: how gas and dust form molecular clouds, how stars are born from these environments, and how stellar evolution and feedback return material back to the interstellar medium. Stars are born
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Gained: Students will study sustainability in computing, including integrating AI with real-time energy data and carbon monitoring, contributing to low-impact, sustainable cloud operations. Distributed
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-scale AI model training, resource orchestration, cloud/edge computing, high-performance computing, or energy-efficient computing. Monash University strongly advocates diversity, equality, fairness and
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analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows
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to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML inference. Problems: In order to enable
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. "Studying the origin of the new discovered class of weak CN stars in the Magellanic Clouds using stellar variability" "How do stars merge? Studying the merger between low and intermediate-mass main-sequence
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, and human-explanation agent. The system may be tested in controlled environments such as simulated enterprise networks, containerized cyber ranges, vulnerable applications, cloud workloads, or IoT-style
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latency, increase throughput, and enable real-time resource management, preparing them for impactful roles in AI, cloud computing, and large-scale system design. A practical example of this project includes