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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
their assigned duty station. The Data-Driven EnviroLab (DDL) is an interdisciplinary and international research initiative based at UNC’s IE that is redefining how data is used to tackle the world’s most pressing
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standard operating procedures (SOPs). Employee may be required to work with animals/tissues infected with HIV/SIV virus, and other experimentally used agents. The work schedule for this position is a 4-day
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accredited animal research facilities to support animal model development. For more information about the MRIID, please visit https://usamriid.health.mil/. About ORISE This program, administered by Oak Ridge
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learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised learning Federated learning The Postdoctoral Researcher will be primarily based
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research projects. Developing innovative methodologies for analyzing massive datasets in both cloud-based and on-premises environments, including experience in building agentic AI tools that integrate
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intelligent robotic behavior across diverse environments. The position involves developing and testing embodied intelligence systems that combine vision-language-action (VLA) modeling, robotic perception and
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virulence and host resistance, as well as characterizing microbial biocontrol agents. The successful candidate will employ resistance screening of hosts, transcriptomic and genomic profiling to compare
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AI agents (cloud-based and edge AI) and related tools for cold supply chains. Participate in and/or lead stakeholder engagement efforts. Supervise and collaborate with graduate students in laboratory
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, Associate, or Full Professor whose research and teaching advance core AI and ML theory, foundation models and generative AI, reinforcement learning and autonomous agents, hardware and systems for AI, edge and
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-tracking, behavioural data). Your team You will collaborate with several GRS colleagues who have expertise in methods and tools for spatiotemporal analysis of complex land systems (including agent-based