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Field
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data Design agentic frameworks using In-Context Learning and Chain-of-Thought prompting for transparent historical inference Develop Historical Hypotheses in collaboration with (other) expert humanists
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should possess experience in 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
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-learn); familiarity with data engineering and infrastructure (PostgreSQL and/or NoSQL, REST APIs, Docker, Git). Differentials: large language models, AI agents and/or semantic databases/graphs. Position
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synthesis of novel antibacterial agents. The successful candidate will join an interdisciplinary team working to combat multi-resistant infections through innovative chemical strategies. The postdoctoral
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About the Opportunity The successful candidate will contribute to an ambitious project developing perceptual AI agents that assist humans in daily activities through behavioral understanding and
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therapeutic agents, using cell-based models as well as patient-derived xenograft models of liver cancer. This position is suitable for a highly motivated self-starter who excels in a dynamic environment
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standards regarding use of isotopes, chemicals, infectious agents, animals, human subjects, and the like. Choose Duke. Duke Surgery is #1 in NIH Grant Funding. We are an internationally recognized leader in
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. You will then develop a procedure to translate these quantified relationships to a predictive agent based model for the investigation of animal movement behaviour under future climate scenarios. We also
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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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supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based