298 computer-programmer-"https:"-"https:"-"https:"-"U.S" Fellowship positions in United States
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, machine learning and AI, statistical computing, big data and AI applications and prediction in biology, medicine and infectious diseases. Potential research projects include (but are not limited
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of developmental and/or regenerative neurobiology, neuronal cell biology, and/or nervous system disease involving the cerebral cortex to join an ongoing research program focusing on the developmental, degenerative
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computer science Special Instructions Applicants should include a full CV, cover letter summarizing your experience, list of reference contacts (minimum of 3), and up to 3 publications. Contact Information Marina
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. Processes, organizes and summarizes data, reporting results using a variety of scientific, word processing, spreadsheet or statistical software applications or program platforms including R, SAS, Python, and
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medical student, researcher, or medical professional to apply for a year-long research fellowship in our program. This position will offer a vast array of research and educational opportunities
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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conditions and policy outcomes. Desired areas of expertise include: dynamic and complex systems, agent-based modeling, computer programming (familiarity with R, Python, Netlogo), statistical analysis
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assessment, and ambulatory behavioral assessments to precisely track brain and cognitive change over short intervals. The program of research seeks to understand individual differences in aging trajectories
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in Python and/or R Experience with cloud computing and high-performance computing environments Ideal Candidate Profile We are seeking a computational biologist, biostatistician or bioinformatician with
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to program development, impact measurement, and continuous improvement in academic environments. Awareness of emerging trends in pedagogy and their implications for student learning and faculty development