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Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and interpretable AI methods (SHAP, Integrated
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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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following programming languages: Java, JavaScript, Python. Demonstrated proficiency with one or more of the following frameworks: React, Spring, Hibernate. Understanding of cloud-based infrastructure, system
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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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-based ML platforms (e.g., Azure, AWS, Databricks) and version-controlled collaborative development environments (e.g., GitHub). Strong programming proficiency in Python; experience with ML frameworks
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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