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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly skilled workforce
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; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL
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, genomic datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding
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-performance computing. Additional experience in DNA biophysics, machine learning, and optimization is preferred. This job description intends to provide a representative and level of the types of duties and
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. GLW faculty draw on the fields of anthropology, economics, history, law, labor relations, political science, and sociology in their research and teaching. To learn more about the faculty in
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, or machine learning experts to create predictive virtual 3D mammalian embryos for human health, especially congenital heart diseases. We welcome applicants with expertise in genomics, developmental biology
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
, and public service. Position Summary Develop highly curated datasets of chemical bioactivity and build statistically validated and externally predictive machine learning models of these activities
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and experience in natural language processing, machine learning, and deep learning. • Strong written and oral communication skills in English. • Ability to work independently and as part of a team
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. Mathematical techniques include qualitative and numerical analysis of deterministic and stochastic dynamical systems, optimal control, network models, and machine learning. Preference will be given to candidates