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methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems. Candidates
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Perturb-seq, with our integrative analyses often employing computational approaches such as agent-based modeling to deconstruct gene-regulatory networks and predict system behaviors. As a postdoctoral
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interests are interdisciplinary modeling (ideally using economic production theory, more specifically Data Envelopment Analysis, system dynamics modeling/agent-based modeling, and/or Artificial Intelligence
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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
, and Experience - PhD in Computer Science, Computing, Statistics or Data Science, or related disciplines - Demonstrated hands-on experience training, fine-tuning, or adapting LLMs / generative AI models
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) and their relationships with neighboring tissues in pathophysiological conditions. We utilize in vivo mouse genetics, live imaging, 3D organoids, genome-wide Cas9/Crispr based functional genetic
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agents for X-ray spectroscopy by integrating large language models (LLMs) with physics-aware spectroscopy workflows. The researcher will work closely with a multidisciplinary team of X-ray physicists and
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-informed neural networks (PINNs) for integrating mechanistic constraints into ML frameworks, and creating LLM-based agents to assist with mechanistic model construction and knowledge curation. Track B
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, PyTorch, JAX etc. Experience with modern AI concepts such as large language models (LLMs), vision-language models (VLMs), model context protocol (MCPs), and the development of agentic AI tools. Skill in
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data