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demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must
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). Ability to use Chrome DevTools or similar browser debugging tools. Understanding of web application architecture and REST APIs. Basic scripting skills (bash, Python, or JavaScript). Familiarity with
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. This position is a benefits-eligible, two-year term appointment through June 30, 2028. Core Responsibilities Design, build, and maintain ML/AI systems and research software in Python and C/C++ Develop and
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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, Python, etc.), and working knowledge of data management protocols. Experience with exposome or environmental data, multiomics integration, and exposure-wide association studies (ExWAS) and GIS preferred
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appointment). Strong background in statistical or machine learning methodology, optimization, or high-dimensional data analysis. Proficiency in R or Python; experience with deep learning, causal inference
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contributions. Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models. Strong programming skills in Python and experience building and maintaining research code
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, epidemiology, environmental or population health) Familiarity with statistical software environments and workflows (e.g., R, Python, reproducible research tools) sufficient to understand project needs and
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, epidemiology, environmental or population health) Familiarity with statistical software environments and workflows (e.g., R, Python, reproducible research tools) sufficient to understand project needs and
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with computational environments for ’omics data manipulation (command line, Python, R, etc.) * Deep knowledge in at least one relevant subdiscipline, i.e. bioinformatics, microbiology, microbial ecology