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Description The Buckler/Romay Lab at Cornell University is seeking a Postdoctoral Associate to advance research on maize and grass molecular diversity using genomic large language models (AI). The goal is to
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). Contribute to sport business and operations analytics projects as needed (e.g., ticketing, fan engagement, and scheduling). Build and maintain data pipelines and models using R, Python, SQL, and cloud
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, Criminal, Education Working Conditions: The position is based on site in Cambridge, MA. There may be an opportunity for a hybrid work model compliant with Harvard University policies. It is anticipated
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in RNA-based work, reporter assays, and cell culture. Experience with animal models is highly desirable as well as understanding of bioinformatics and RNA structure concepts, enabling effective
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
Position overview We are seeking applicants for an 18-months postdoctoral fellowship focusing on the development of an AI-based phenotyping system for high-throughput screening for aphid
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MLOps pipelines, CI/CD workflows, and containerized deployments for on-premise, and hybrid DoD environments. Design API-based AI services for integration into command-and-control systems, decision support
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other user schedules Track assessment information and incorporate information into Asset Renewal model Contribute to project scope development based on assessment program and desired outcomes Using
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented
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environmental quality. The fellow will contribute to field and laboratory studies, data analysis and modeling, publication of results, and development of decision-support tools for producers, advisors, and agency
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a high level of evidence, yet their narrow eligibility criteria and controlled settings often limit generalizability. Advances in causal inference and the increasing availability of real-world data