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. • Strong background in autonomous robotic systems, unmanned aerial systems (UAS), or multi-agent robotics. • Demonstrated expertise in one or more of the following areas: o Counter
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Architecture: Architect, build, and evaluate multi-agent systems powered by LLMs and machine learning, incorporating Orchestrator and Memory agents, vector databases, and knowledge graphs to enable complex goal
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applied to biomedical data. Experience or strong motivation in developing LLM-based applications, AI agents, multi-agent systems, RAG, tool-calling workflows, or autonomous scientific workflows. Familiarity
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multi-agent robotic systems, drone swarms, or autonomous decision making. Experience in integrating prediction, planning, decision-making, and control modules within a simulation or experimental framework
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agents for data analysis, and the design of interactive systems that keep human analysts in control of AI-assisted sensemaking. Responsibilities Conduct research on agentic visualization: generative AI
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systems for aligning real-world health data to standards like OMOP CDM, FHIR, and UMLS Agent-based workflows that explain, refine, and adapt semantic mappings over time Hybrid architectures that combine
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members to ensure all project deliverables are met. Collaborate with the PI, Co-PIs and NTU team to design and develop a robust and scalable software system for autonomous agents navigating immersive 3D
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at Harvard Medical School in Boston, Massachusetts. The work of the Zitnik lab is focused on Artificial Intelligence for Medicine and Science. The selected candidates will lead research in generative
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field. Hands-on experience in designing and implementing software systems involving multi-agent frameworks. Familiarity with immersive platform development (e.g., Roblox, Minecraft), including scripting
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). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents, or similar. Solid expertise in computer vision techniques, transformer architectures, and multi-modal