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machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes thus
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that combines modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks
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. Applicants must have experience in the handling and organization of large data sets. Familiarity with analysis of panel or longitudinal data and working with multilevel models is valued. Additional skills with
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include designing and executing experiments to interrogate host-microbe interactions, analyzing and interpreting microbiome sequencing and/or metabolomics data, developing novel computational or analytical
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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 design nitrogen-efficient maize
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) • Big data pipelines, distributed computing, and geospatial data processing • Python, R, SQL/NoSQL, containerization (Docker), Kubernetes • API development and web-based analytics tools • Systems
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, or related fields and the successful applicant must have advanced quantitative and biological skills and an understanding of basic disease dynamics. The work will explore large datasets on bat viruses we have
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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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efforts in AI or other related topics. Applicants must have experience in the handling and organization of large data sets. Familiarity with analysis of panel or longitudinal data and working with
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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