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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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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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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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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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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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, 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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findings at meetings and/ or conferences. Lead the definition and documentation of requirements, architecture and design of secure, scalable, asynchronous, agentic systems, based on knowledge of principles
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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
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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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analysis of mobility behavior, the application of agent-based models and road space analysis, particularly for road safety. Research on walking and cycling as a cross-cutting issue has a high priority in all