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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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work alongside leading researchers, industry partners and stakeholders to accelerate the translation of breakthrough ideas into secure, cloud-native products. You'll combine hands-on technical leadership
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model, uplift service delivery, and ensure the function is positioned to respond effectively to an evolving threat landscape spanning cloud, identity, SaaS, supply chain, and AI-enabled risks. You will
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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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the Monash campus network Drive modernisation programs, including hybrid cloud integration and data centre optimisation initiatives What You'll Bring Demonstrated leadership of enterprise-scale network
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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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theoretical colleagues. All research takes place within our dynamic particle physics research group with academics and postdocs, as well as graduate and undergraduate students. Some work will be purely