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Project descriptionAutonomous systems are intelligent agents—such as robots, vehicles, or drones—that can sense their environment, make decisions, and act independently. When multiple such agents
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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
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in AI: Generative Diffusion & 3D/4D Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Semantic-Aware Compression & Network Information Theory
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extended from cloud solutions (such as OpenLLMetry), the research question is how to identify anomalies in collected information that can come from multiple AI services either invoked manually by users or by
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, collaboration, and effective teamwork between human workers and AI agents. The research will employ Participatory Design (PD) approach, emphasizing a democratic approach to technology design. This will require
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on these comparisons, you will create agent-based models (ABMs) that define interaction rules based on observed similarities and differences in events [4], with a focus on the specific role of individual differences
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and 8.3% year-end bonus; a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU. In addition to the terms of employment external link laid down in the CAO
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bonus; a pension scheme, partially paid parental leave and flexible terms of employment based on the CAO NU. In addition to the terms of employment external link laid down in the CAO NU, Utrecht
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robots -Distributed task planning for collaborative missions -Behaviour tree based multi agent collaboration -Reactive task allocation in large scale missions For further information about a specific
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on an important topic in a well-funded multi-disciplinary international training network. The training involves multiple activities, in addition to your research, and secondments across our partners. Overview