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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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applying data science principles and techniques to biomedical datasets. Applicants should be comfortable using coding skills such as SQL, R, and Python to extract data from databases, clean it, and analyze
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coding/scripting (e.g., R, Python) as evidenced by application materials. NOTE: To be appointed at the Postdoctoral Researcher title, it is necessary to have the PhD in hand. Appointments made without a
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with electronic health record (EHR) and/or clinical data. Proficiency in Python, with strong coding and debugging skills. Experience with deep learning frameworks such as PyTorch, JAX, TensorFlow
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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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mathematical algorithm in codes and work on submitting papers Analyze theoretical and numerical results, summarizing findings in reports. Write and submit papers for publishing in peer-reviewed journals and
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Organization U.S. Department of Energy (DOE) Reference Code DOE-OREM-ESPSE-24 How to Apply A complete application consists of: An application Transcripts A current resume including academic history
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: Ph.D. in atmospheric science, meteorology, or climate science Proficiency in Python coding and visualization Familiarity with large reanalysis datasets Preferred Qualifications Work experience beyond
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Collection – Conduct in-depth interviews and organizational observations to gather high-quality qualitative data. Data Analysis – Use ATLAS.ti or similar tools to code and analyze data, generating theoretical
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of the research group and others as described in the Duke University School of Medicine Honor Code of Professional Conduct and other relevant conduct policies pertaining to other schools at Duke University