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, efficiency enhancements result in rebound effects due to feedback between enhanced efficiency and food system economics. The successful candidate will collaborate in a multi-disciplinary, international team
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on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks and system
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PostDocs on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks
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and multi-agent systems for conservation genomics; perform feature engineering using chromosome-level genome assemblies. 15% Writing: Contribute to scientific manuscripts, progress reports, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
contribute to the development of next-generation AI software within the ACM Lab that leverages LLMs, multi-agent reasoning, and advanced machine-learning techniques to enable rapid, scalable analysis of large
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for collaboration, mentorship, and intellectual exchange. Anticipated Division of Time 80% Research: Build, train, and evaluate cutting-edge AI models and multi-agent systems for conservation genomics; perform
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, and agent-based modeling for care delivery Health equity, patient access, and system resilience Multi-modal data integration using EHR, claims, environmental, and behavioral datasets The successful
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simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling Large-scale computing, cloud-native analytics workflows, and data engineering for mobility platforms AI/ML
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, fibrosis-enriched imaging phenotypes, multi-omic causal discovery, and AI-enabled target prioritization. The fellow may also contribute to projects related to scientific discovery agents and AI-assisted