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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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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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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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scaling cloud-based activity-based mobility analytics systems (AWS/Azure/GCP) for large multi-city datasets Enhancing and deploying computational platforms such as Cornell TEAM-Cities, CATChain, uTECH, etc