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investigates how to adapt, modify, and optimize decentralized nature-based solutions (NbS) and green-grey hybrid infrastructure—encompassing sanitation, drainage, and flood mitigation. The candidate will address
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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
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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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of the AAAI Conference on Artificial Intelligence (Vol. 26, No. 1, pp. 267-273). - Blau, T., Bonilla, E. V., Chades, I., & Dezfouli, A. (2022, June). Optimizing sequential experimental design with deep
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Explore the asymptotic behavior of MML estimators, including consistency, convergence rates, and their connection to information geometry. Investigate how the optimal data space partitioning relates
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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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on social dilemmas, i.e., situations where poor group outcomes arise from optimal individual choices. We use this framework to study: Multi-agent Systems and AI, Social Systems, and Models in Biology and
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to new logics. [1] Rajeev Gore: Tableaux Methods for Modal and Temporal Logics. Handbook of Tableau Methods, Kluwer, 1999. [2] Rajeev Gore, Florian Widmann: Optimal and Cut-Free Tableaux for Propositional