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: System-Level Impact Analysis of IAM using Agent-Based Models Supervisor: Prof. Dr. Regine Gerike, Chair of Mobility System Planning and co-supervised by at least one additional professor
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candidates may include: 1.Energy modelers with expertise in integrated assessment modeling, energy system modeling, or agent-based modeling, who are eager to incorporate socio-political dynamics
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a key role in building and integrating of AI agents into gaming scenarios (e.g., gameplay, interactions, procedural content generation, dynamic narratives), and integrating a multimodal detection
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based on an orchestrator and a set of intelligent agents for information retrieval and processing; - Validation of proposed solutions through empirical studies in representative decision support and
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networks. Participation in national and internationally funded research projects. Contribution to advanced digital twin and agent-based simulation platforms. Opportunities for interdisciplinary collaboration
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. AI-based performance assessment in early design, integrating rapid analysis tools, multi-criteria performance estimation, and surrogate modeling. Human–AI collaboration in design, including agent-based
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Snowflake data platform. This role requires deep expertise in Snowflake's core capabilities-including data modeling, performance tuning, ELT pipelines, security controls, and orchestration-while also
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candidate will develop and test novel user interfaces that integrate state-of-the-art Large Language Models (LLMs) with novel logic-based multi-robot planning algorithms. This work will be evaluated through
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verification and Large Language Model (LLM) safety, focusing on extending state-of-the-art logic-based automated reasoning tools such as ESBMC (https://github.com/esbmc/esbmc ) to address safety and reliability
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g