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physical environments. This position focuses on research at the intersection of computer graphics, generative AI, and robotics, encompassing topics such as generative modeling, reinforcement learning, multi
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, multi-agent systems, and agent based modelling) and energy systems (energy modelling, renewable energy, energy management, and energy in agriculture). The position will be under the direction of Dr. Karl
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adaptation of existing approaches for scientific applications; (ii) Large Language Models (LLMs) and multi-modal foundation Models (iii) Agentic AI techniques for scientific domains; and (iv) techniques
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three years ago; - Relevant experience in conducting research in IoT, Multi-agent Systems, Asset Administration Shells, and RAMI4.0; - The candidate's training and experience must be appropriate
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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) Research Experience: Relevant research experience in related problem domains, or research experience in scheduling, routing, and multi-agent pathfinding (MAPF) algorithms - Experience across multiple areas
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per year, if mutually desired. Research will focus on multi-level investigation of safety-critical human-in-the-loop systems that collaborate with automated/autonomous decision-aid technologies. Desired
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, starting as soon as possible, to support different projects and the supervision of doctoral researchers in the team. The Mobilab team is a dynamic group with broad research competences and interests, with
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The focus of the project is artificial intelligence (AI) and its relation to robotics and embodiment. Embodiment plays a significant role in learning in AI by enabling cognitive agents to acquire actively
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multi-agent pathfinding (MAPF) algorithms - Experience across multiple areas is a strong plus; Experience developing ML-based optimization approaches is a strong plus; A strong publication track record is