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cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science. The purpose of this project is to use game theory and computational
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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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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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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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. "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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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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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
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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