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the environment. Knowledge of characteristics associated with mild cognitive impairment capturable with digital interfaces. Experience with agentic large language models Experience in artificial intelligence
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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will lead research at the intersection of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling
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, supply chain resilience, and lifecycle sustainability. The successful candidate will lead and conduct independent research in optimization, systems modeling, agent-based modeling (ABM), and network
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sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling to evaluate the technical, environmental, and socioeconomic implications of transitioning to resilient crops in
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engineering. Familiarity with optimization, agent-based modeling, or systems dynamics. Prior work in interdisciplinary, international, or applied research collaborations. Strong programming and data integration
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for Risk Forecasting, Large Language Models (LLMs), and Human-in-the-Loop AI Systems. Our aim is to advance AI for Operations by integrating next-generation AI agents and LLMs with real-world operational
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will lead research at the intersection of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling
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will lead research at the intersection of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling
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function independently to develop therapeutic agents against mutant p53 and other oncoproteins using artificial intelligence, monoclonal antibodies, and DNA vaccines. The positions offer a unique team-based