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; for example, a Ph.D. in social psychology with no experience in engineering or computational approaches would not be acceptable. Experience with programming (Python, Java, Javascript, PhP, and others) Training
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experience (Python, C/C++/C#/etc.). Fundamental understanding of, and practical experience with AI / machine learning / deep learning (TensorFlow, Keras, scikit-learn, PyTorch, etc.). Experience with big data
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of the projects. Basic knowledge in coding language, including but not limited to, basics of Linux, MATLAB, Python. Ability to carry out both wet-lab work for sample preparation and dry-lab work for image analysis
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Open Source Geospatial software and libraries, e.g. GDAL or other open source geospatial packages under OSGeo; Experience in programming with multiple languages (e.g. Java, C/C++, Python) for geospatial
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in coding, i.e. MATLAP, Python, etc. Salary Range $61,008+ depending on NIH level Working Conditions May work around standard office conditions. Repetitive use of a keyboard at a workstation. Required
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including advanced coding in R and/or Python, -An interest and track record in publishing in top academic journals; -Proficiency using GitHub and coding languages, and other modern data management, sharing
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, instrumental variables, treatment selection, power analysis Proficiency in R, Python, or Stata for statistical analysis Experience with SQL and data management Familiarity with Generative AI tools and LLMs
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, control theory, or a related field. Strong statistical understanding and a talent for data analysis and visualization using Matlab or Python are expected. Specific experience with experimental design
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groups/populations. Preferred Qualifications PREFERRED QUALIFICATIONS: 1. Demonstrated experience with Linux/Unix environment, Python, and PyTorch. 2. Demonstrated experience with programmable network
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groups/populations. Preferred Qualifications PREFERRED QUALIFICATIONS: 1. Demonstrated experience with Linux/Unix environment, Python, and PyTorch. 2. Demonstrated experience with programmable network