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Division of Time 80% Research: Build, train, and evaluate cutting-edge AI models and multi-agent systems for conservation genomics; perform feature engineering using chromosome-level genome assemblies. 15
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developers of Jarvis . Anticipated Division of Time 80% Research: Build, train, and evaluate cutting-edge AI models and multi-agent systems for conservation genomics; perform feature engineering using
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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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systems • Healthcare operations, resource allocation, and workflow optimization • Network, graph, and agent-based modeling for care delivery • Health equity, patient access, and system resilience • Multi
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support research in: • Transportation systems modeling and simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling • Large-scale computing, cloud-native analytics
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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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analysis of effective tutoring moves that promote student motivation, engagement, and learning Development and evaluation of approaches to AI tutoring agents that build on high-quality data (e.g., training
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, engagement, and learning. Design and evaluation of AI tutoring agents leveraging high-quality tutoring data (e.g., training tutors directly on tutoring data; exploring a wide range of fine-tuning and
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maps to guide farmers, extension agents, and policy partners, and (4) collaborate on co-production activities with community and stakeholders. The position includes opportunities to lead and co-author
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) integrate crop, hydrologic, and GHG/energy models to quantify trade-offs among yield, water, emissions, biodiversity, etc., (3) create decision-support tools and maps to guide farmers, extension agents, and