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interest in agentic AI, online learning and optimization, and applications in economics. The full-time positions (100%) are initially offered for two years, with the possibility of extension, depending
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software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new code via
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-motivation and interest to learn new skills Great to have: Experience programming in Python, Julia, or C/C++ Experience with Mathematica Experience with finite element methods, agent-based simulations, and/or
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) develop participatory planning methods based on the technical outcomes from the digital twin to create future scenarios for responsible mobility that are technically well-grounded and at the same time
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the MATSim agent-based transport simulation framework. The main task is to enable simulated agents to choose transportation modes, such as car, bus, bike, or walking, based on real-time feedback from