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Field
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study team members. Collaborates with the Principal Investigator and implementation team to develop, refine, and evaluate the Pocket PCIT+AI conversational agent, contributing to content development
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-supported educational tools, including large language model-based agents, with attention to instructional quality, learner interaction, and responsible use. Experience using Arduino microcontrollers
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learning, foundation models, including large language models, agentic AI, multi-agent AI systems, transfer learning, self-supervised learning, and federated learning. The Postdoctoral Researcher will be
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elegant APIs and intuitive, welcoming, user-centered interfaces; Definition and development of agentic (user)interfaces to OpenML; Contribute to OpenMLs’ federated position ensuring that the platform
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free of adventitious agents (AAs) such as bacterial and fungal contaminants. Current AA detection methods require lengthy culture times and are not suitable for continuous manufacturing, where rapid
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health records. Medical Reasoning and Grounding: Developing advanced post-training and alignment techniques (RLHF, DPO, GRPO) to ensure foundation health models reason accurately and safely. Agentic
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advanced post-training techniques to ensure foundation health models reason accurately over medical knowledge graphs and clinical instructions while verifying factuality and safety boundaries. Agentic
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Experience with developing AI/ML Deep Learning models, working with agentic workflows, model training and other emerging AI techniques and tools Programming experience in Python, C++, or similar scientific
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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing
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qubits and next-generation quantum sensors. AI Integration: Collaborating with AI experts to integrate machine learning and autonomous agents into the crystal growth and device development workflow