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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); today published
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Field
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high infrastructure costs. We will explore (1) learning-based techniques (e.g., LLMs, agents) to capture the intent behind code changes, (2) defining new metrics for test "quality" that go beyond code
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to establish a roadmap, (2) developing models and benchmarks for LLM-based refactoring, (3) designing autonomous agents, and (4) conducting studies to analyse real-world impact. We are committed to creating a
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with unprecedented detail, enabling dynamic, data-driven insight into the recoverable value of materials. Agentic AI systems will be designed to autonomously explore and propose optimal dismantling
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that the developed methods are robust, adaptable, and grounded in real-world practice. You will apply advanced techniques such as agent-based modelling, quantitative resilience assessment, and risk analysis to
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-based modelling, quantitative resilience assessment, and risk analysis to simulate and optimise resilience strategies. The framework will be tested and refined through pilot studies in collaboration with
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hypothesis is that it is possible to design a mixed network by simulating how to serve a given demand with an on-demand ridepooling service, tracking the vehicles’ routes, and allocating fixed lines wherever
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. Advertising images can also link products to ideas of success. Thus, these forms of communication are not merely tools for conveying messages, but powerful agents that sculpt our society, influence our
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the vehicle fleet and the multi-objective design of the mixed transporation network. Our key hypothesis is that it is possible to design a mixed network by simulating how to serve a given demand with an
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data