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Field
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expertise in agent-based modelling. The models we build will have an interface with community stakeholders and mobility service providers, so we are particularly seeking applicants who are comfortable in
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argumentative agentic AI approach for chemical development settings based on reinforcement learning but able to shape rewards with the help of ontological knowledge as well as expert knowledge from humans and
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: Train Your Dragon: Fine-tuning Large Language Models to Create Agentic AI for Automatic Assessment Generation Project Introduction: This position involves two closely related research strands
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on Multi-Agent Agentic AI architectures. Develop AI models and AI-based applications, tools, and software components collaboratively. Build, maintain and operate a private 6G cloud testbed primarily based
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-driven, knowledge-based agentic systems for automated alert triage and investigation in SOC environments, contributing to the AutoSOC platform Build explainable AI models that integrate planning, memory
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period: six months] Duties The appointees will assist the project leader in the project - “Developing an LLM-based multi-agent language learning platform: Personalized tutoring and contextual simulation
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a key role in building and integrating of AI agents into gaming scenarios (e.g., gameplay, interactions, procedural content generation, dynamic narratives), and integrating a multimodal detection
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(RAG) pipelines over the scientific literature and curated knowledge bases, allowing agents to perform automated literature studies and ground their outputs in established knowledge. Design systematic
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, process mining, conformance checking, and token-based what-if analysis. Build and integrate LLM-enabled multi-agent components for expert elicitation, structured questioning, explanation, and knowledge
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environmental simulations in game engines and integrate them into a persistent multi-user environment platform. Develop and refine RAG-based AI agents using Google AI Studio Pro. Manage the workflow of converting