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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
Python or R is preferred. Ability to analyze multi-omic data, including microbiome data, would be an advantage. Ability to build algorithms and data pipelines would be ideal. Create a Job Match for Similar
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formula. Preferred Qualifications Familiarity with Python, R and MATLAB. Experience with machine learning/deep learning. Master’s degree in a related field. PhD or near to completion of a PhD (or equivalent
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the implementation of models in e.g. Excel, Stata, Matlab, R, Python, or SAS. To be considered for the position, students should have achieved top grades (10 or 12, or international equivalent) in most relevant
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BioEngineering Experience with fluidics devices, 3D printed devices, 3D cell culture models and bioinformatics/computational biology (e.g., R, Python) is an asset Team player Proficient knowledge in English is
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methods (e.g., SGTCCA-NET) for the project’s needs. Develop and implement novel statistical approaches for multi-omics network analysis. Update and improve existing R code to incorporate methodological
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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(or similar) on concrete (or similar quasi-brittle materials). Experience with materials- and large(r)-scale component testing. Experience in laboratory testing and the use of conventional sensing equipment
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information, please visit our website: www.markwulab.net . Representative publications: 1. Lee, S.-S., Liu, Q., Cheng, A.H.R., Kim, D.W., Boudreau, D.M., Mehta, A., Keleş, M.F., Fejfer, R., Palmer, I., Park
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related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale genomic data analysis. You
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analysis according to the study protocol and statistical analysis plan. You should have experience in epidemiological analysis using routine healthcare datasets, along with advanced skills in coding in R and