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) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and
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, MetaPhlAn, or similar). Proficiency in programming languages for data analysis (e.g., R, Python). Experience with mass spectrometry-based metabolomics approaches, including LC-MS/MS. Experience operating
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Integrated Pest Management (Cornell IPM; https://cals.cornell.edu/integrated-pest-management ), is housed at Cornell AgriTech and is a translational research program of the Cornell University College
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programming languages for data analysis (e.g., R, Python). Experience with mass spectrometry-based metabolomics approaches, including LC-MS/MS. Experience operating, maintaining, and troubleshooting LC-MS/MS
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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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platforms (e.g., Azure, AWS, Databricks) and version-controlled collaborative development environments (e.g., GitHub). • Strong programming proficiency in Python; experience with ML frameworks such as PyTorch
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proficiency in Python; experience with ML frameworks such as PyTorch, TensorFlow, or equivalent. Publication records in peer-reviewed journals. Demonstrated ability to communicate complex technical findings
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demand forecasting or behavior modeling Computing & Data Systems Cloud computing (AWS, Azure, GCP) Big data pipelines, distributed computing, and geospatial data processing Python, R, SQL/NoSQL
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modeling in agricultural or environmental systems. • Proficiency in R, Python, or related analytical tools. • A record of peer-reviewed publications. Preferred Qualifications • Experience with livestock
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analytical processes written in R, Python, or other languages. The Data Engineer will migrate current local processes to cloud services, as well as write and maintain system documentation. The Data Engineer