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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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or MD/PhD Strong programming skills, preferably in R and/or Python Previous expertise and/or interest in single-cell sequencing technologies, bioinformatics, spatial analyses, and generative AI is desired
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Python and Julia with experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Experience building complex software systems, preferably with industry experience. Research background in
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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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. Proficiency in programming (Python, Julia), and high-performance computing (provide evidence with specific examples) Ability to work independently and collaboratively. Strong written and oral
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Expertise and hands-on experience using LLMs, Machine Learning, Deep Learning frameworks, Natural Language Processing, etc. Proficient programming skills in Python and R and working knowledge in SQL Strong
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building and deploying prediction models • Strong data science coding skills in programs and languages such as Python, R, Stata, and SQL • Experience with research in healthcare domains preferred
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to the microbiome/metabolome. Superb quantitative background, strong coding skills (e.g., Python, R, MATLAB). Strong computer skills, experience with databases and scientific applications, and ability to quickly
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA