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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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engineering projects is increasingly complex, particularly when balancing human expertise and Large Language Models as collaborative agents. This project aims to propose a reputation-based agentic Artificial
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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
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. Experience developing and fine-tuning Large Language Models (LLMs) and transformer-based architectures. Practicalexperience building AI agents, including agentic workflows, autonomous decision-making pipelines
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-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed
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fungal infections. Emerging research is exploring radiometals conjugated with targeting molecules for in vivo imaging of infections via PET or SPECT. These agents can also be linked to therapeutics
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based on an orchestrator and a set of intelligent agents for information retrieval and processing; - Validation of proposed solutions through empirical studies in representative decision support and
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and responsibilities The Project Expert performing as Senior AI Solutions Developerwill: Develop and implement advanced AI models, including deep neural networks, transformers, LLM-based systems
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integrated assessment modeling, energy system modeling, or agent-based modeling, who are eager to incorporate socio-political dynamics into their models; or 2.Computational social scientists with experience in
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented