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applying interpretable AI / machine learning / deep learning / information-theoretic methods and algorithms in the context of multiscale biological networks, ranging from molecules (protein chemistry) to
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to have exceptionally strong backgrounds in GIS, computational science and expertise in other sciences (biology, ecology, physics/engineering, etc.) and programming (e.g. R, Python, etc.). The successful
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); mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is
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to Contact With Questions Focus Areas Explore All Focus Areas Arctic and Antarctic Astronomy and Space Biology Chemistry Computing Creating a STEM Workforce Earth and Environment Education and Training
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understanding of computational algorithms and statistical modeling and a working knowledge in molecular biology, microbiology, and microbial ecology • possess a working knowledge on genomic sciences and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
multi-omic data, including microbiome data, would be an advantage. Ability to build algorithms and data pipelines would be ideal. Required Qualifications, Competencies, and Experience PhD in