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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
concurrent projects. Knowledge of 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
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in MATLAB or Python for simulation/modeling and data analysis. • Experience with COMSOL Multiphysics and/or Lumerical is a plus. • Experiment: Familiarity with ultrafast optics, terahertz generation
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. •Proficiency in Python, R, AI/ML frameworks, and bioinformatics pipelines for high-throughput data analysis and decision support in plant breeding. •Experience developing AI-enabled decision support tools
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. •Proficiency in Python, R, AI/ML frameworks, and bioinformatics pipelines for high-throughput data analysis and decision support in plant breeding. •Experience developing AI-enabled decision support tools
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languages, preferably Python; excellent communication and presentation skills, including the ability to present analysis in a clear and compelling manner for various stakeholder audiences; strong
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learning; strong programming skills in Python and at least one systems-level or real-time programming language such as C, C++, or C#; experience with modern deep-learning frameworks such as PyTorch or
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experience in machine learning or AI; proficiency in Python and a deep learning framework; experience designing and evaluating computational models; and demonstrated scientific writing and collaboration
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experience in machine learning or AI; proficiency in Python and a deep learning framework; experience designing and evaluating computational models; and demonstrated scientific writing and collaboration
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multimodal learning; strong programming skills in Python and at least one systems-level or real-time programming language such as C, C++, or C#; experience with modern deep-learning frameworks such as
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genomics, evolutionary biology, machine learning, or structural bioinformatics Strong programming skills in Python and proficiency in Unix/Linux environments Demonstrated record of publications in peer