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Field
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by working to develop novel algorithms on finite element method, isogeometric analysis, geometric modeling, machine learning and digital twins to study various applications such as computational
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)-statistics, (applied) mathematics, or a related STEM field. Prior working experience with EHR data, machine learning, NLP, bioinformatics, and large language models (LLM) is preferred. In particular
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GROMACS, AMBER, MARTINI, OpenMM, or similar tools ii) Experience with machine learning or AI experience (e.g., PyTorch, JAX) for RNA modeling and/or drug discovery iii) Experience in coding and scripting
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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experience in one or more of the following: Engineering, automation, and technology-related medical research Experience with computer programming in Python / MATLAB, especially related to deep learning model
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
infectious disease dynamics, epidemic modeling, and/or machine learning and artificial intelligence Preferred Qualifications, Competencies, and Experience * Writing programs and performing analysis in R and/or
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that apply: ☒Sitting at computer work station for long periods of time ☒Standing for long periods of time ☒Risk of back injury from moving/lifting, equipment, or materials ☐Repetitive motion ☒Lifting/Carrying
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
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well as bulk RNA-Seq, Proteomics, and Metabolomics generated from mouse and patient cohorts with rich clinical data - Advanced modeling of arrhythmias using generalized linear models and machine learning