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The Extension Agent for Family and Consumer Sciences will develop, implement, and evaluate a plan of work based on locally identified needs which will lead to improved quality of living for families & individuals
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engineering discipline. Experience: The ideal candidate should have some knowledge and/or experience in several of the following topics (ordered by importance): Large language models and LLM-based agents Multi
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mobile base, an arm, a gripper, a learned policy, a safety module). Each agent runs its own specialized solver and is coordinated to a common, dynamically feasible plan through distributed optimization and
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above) grades. You have a strong background in deep learning. Previous experience with robotics, world models, reinforcement learning or other ML-based techniques for robot control is considered a plus
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for extension. We are looking for someone to join our team of consumer behaviour scientists and agent-based modellers who can bring knowledge of and experience with modelling cognitive or behavioural change
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experience in several of the following topics (ordered by importance): Large language models and LLM-based agents Human-AI interaction, human-computer interaction and human factors Reinforcement learning and
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directions: Long-term Autonomy in Uncertain Environments a. Agentic Planning and Reasoning - Semantic Mission Planning with Foundation Models - Foundation models based task decomposition - Event-driven task re
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deploying an AI project. This includes initial problem specification, data gathering and analysis, model creation, and implementation. Applicants should be willing to learn and use agentic AI to build and
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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model
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include but are not limited to: AI-driven design workflows; machine learning and generative AI models; AI-based performance evaluation and simulation; agentic and multi-agent AI systems; human-AI