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Field
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using gem5 simulator as the main execution backend for the RL agent, while also investigating the potential of RTL simulation and physical RISC-V board as complementary execution nvironments. A key aspect
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robotics, drones, UAVs, multi-robot systems or smiliar, and practical applications of LLMs, agents, RAG, MCP, or fine-tuning. Proficiency in software development (Python, C/C++), skills in data analysis, and
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Learning, e.g., Human-centric Learning from Demonstrations, Shared Autonomy, etc. Robotics and automation using sustainable materials and process workflows Agentic systems for robotics and control Energy
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areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Forecasting Models Basic Qualifications A Ph.D. or equivalent degree
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leads the research theme “Constructing Nonverbal Communication Channels between AI–AI Agents.” Informal inquiries regarding the research topics and directions are welcome. [Work content and job
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learning and LLM frameworks like LangChain and agent-based LLM frameworks, Retrieval Augmented Generation (RAG). Experience with fine-tuning, deploying, and evaluating Large Language Models. Experience with
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, or decision-making in autonomous driving or robotic systems Reinforcement Learning and Agentic Control: Hands-on experience with reinforcement learning, multi-agent systems, or planning-based agents
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the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The agent will be
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Learning, e.g., Human-centric Learning from Demonstrations, Shared Autonomy, etc. Robotics and automation using sustainable materials and process workflows Agentic systems for robotics and control Energy
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databases and retrieval-augmented generation (RAG) and integrate agentic AI systems to meet the demands of large-scale fine-tuning and inference. The postdoc will also work on design and implement agentic-AI