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infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption). Researchers and students will explore innovative approaches to reduce
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for predicting resource demand and optimizing allocations. Skills Gained: Students will work with real-time data analytics, reinforcement learning, and predictive modeling to create efficient resource utilization
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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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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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operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able