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fragmentation. This project seeks to overcome these barriers by integrating BIM-based energy modeling, semantic data models (Ontologies), and Large Language Models (LLM) into the control workflow. The candidate
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on reinforcement learning (RL) for policy discovery in a multi-sector “integrated modeling environment” that connects fast ML metamodels of simulators (e.g., transport, energy, environment, climate events). The aim
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student to work within the ADaM project (Autonomous workflows for Data-driven first-principles Modelling). The project will leverage Large Language Models (LLMs) as active software agents to help automate
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be considered an advantage if you have experience with safety-critical systems, multi-agent autonomy, or learning-based/data-driven/robust/adaptive control under uncertainty, supported by strong