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degradation modes. Evaluating suitable sensor technologies and data sources for acquiring relevant metrics. Developing tools and algorithms to automatically analyse sensor data, assess asset condition, and
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solvers and optimization algorithms for 1 year and 4 months. The Section of Solid Mechanics conducts research and teaching in the fields of structural and materials mechanics, vibration and their active
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an optimal molecular representation (including data procurement) and integrating generative model and binding oracles. Propose an algorithm to bias the generative models towards desirable properties, such as
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electricity price signals, demand-response mechanisms, and time-of-use optimization. AI-Driven Optimization using Reinforcement Learning: Apply RL algorithms to develop and train agents that optimize power
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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs
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, generative design, building performance optimization, digital design methods (e.g., predictive modeling, multi-agent systems and algorithmic techniques for architectural design), digital design epistemologies
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect
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-based topology optimisation and de-homogenisation Adaptive meshing algorithms for topology optimization PDE-driven topology optimisation methods Research fund application Collaboration with industrial