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synthesizing findings with prior knowledge from the scientific literature, a process that today depends heavily on manual expert interpretation. Recent advances in large language models, agentic systems, and
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development processes. Modern software development increasingly relies on AI-based tools (e.g., large language models and autonomous agents) to generate, modify, and evaluate code. While these tools
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are used to enable systematic comparisons between alternative strategies in simulated environments. The project also studies simulation agents operating within these models, including both policy-based
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. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous
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focus on traceability, verification, and reproducibility of AI-assisted software development processes. Modern software development increasingly relies on AI-based tools (e.g., large language models and
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interactions can be generated based on emerging and pre-defined protocols for interaction between agents, between agents and their environment and between agents and humans. By an interaction protocol, we mean a
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also connected to the Wallenberg Initiative Materials Science for Sustainability (WISE, https://wise-materials.org ). WISE, funded by the Knut and Alice Wallenberg Foundation, is the largest-ever
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area of AI security, on the topic of “Memory Poisoning in LLM Agents: Foundations, Attacks, and Defenses”. Your work assignments Large language model (LLM) agents represent the next generation of
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modeling builds on agent-based modeling but focuses on relations, not agents, and emphasizes the role of the researchers and their tools in constituting the model and the generated knowledge (Schlüter et al
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develop a framework based on collaborative AI concept, e.g., Agentic AI or other collaborative Large Language Models (LLMs), that synthesizes actionable intelligence from distributed, heterogeneous data