63 computer-programmer "Data driven Materials Modeling" Postdoctoral positions at Duke University
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, artificial intelligence, and data-driven discovery. Learn more about the iMIND Study Team: https://dukeeyecenter.duke.edu/imind Be Bold. Join a multidisciplinary research team at the forefront of
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Health, TriNetX, or similar oncology datasets. Knowledge of survival analysis, longitudinal data analysis, mediation analysis, latent class analysis, predictive modeling, machine learning, and causal
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position in mathematical modeling, as part of a new NIH-funded Center for Excellence in Multiscale Immune Systems Modeling . This position focuses on the development, calibration, and analysis of
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with experience or interest in machine learning, statistical modeling, natural-language processing, clinical or population-health data, reproducible research, and interdisciplinary collaboration
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, data-intensive research team. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious
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epidemiologic and biostatistical methods, including causal inference, survival and longitudinal modeling, and approaches for integrating multi-level and high-dimensional data. There will be opportunities
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., M.D., D.V.M.) Preferred Qualifications: . PhD in one of these fields: Computer Science, Data Science, Biomedical Informatics, Computational Biology, Information Science or a related field. Experience
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with hierarchical modeling and mixed effects models. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae
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functional muscle modeling, you will help develop innovative disease models that improve our understanding of disease triggers, identify novel biomarkers, and accelerate therapeutic discovery. In this
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candidates will have the opportunity to work in a collaborative and interdisciplinary environment that combines genomics technology development, stem cell and regeneration biology, disease modeling