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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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competitiveness in GM. AMMIC brings together world leading research facilities and expertise to stimulate new investment in research and development (R&D) to address regional and national materials and
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cleaning datasets from recently completed research studies, database management, conducting statistical analyses (SAS or R), review/verification and archival of programs for research manuscripts, and
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). Knowledge of environmental sustainability, soil health, or plant-microbe interactions. Proficiency in the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R
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of proposals and contact with funding organizations. Communicate with all stakeholders including R&D peers, suppliers, and the client.
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meet the essential criteria set out above will not be able to book) To apply today, please visit:https://forms.office.com/r/EBjJpSsQJY Deadline midnight, August 28,however, candidates are encouraged
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preferred. Knowledge of statistical methods, programming (e.g., Python, R), and data quality assurance techniques. Proven ability to work collaboratively in an interdisciplinary environment and engage with
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analysis software (e.g., SPSS, R, NVivo) is highly desirable. · Publication Record: Evidence of scholarly productivity, including publications in peer-reviewed journals. · Communication Skills: Strong
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superb quantitative background, strong coding skills (e.g., Python, R), expertise in infectious disease modeling across multiple pathogens, expertise with large datasets and statistical analysis, and high
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standard imaging analysis method including use of Python (NumPy/SciPy/PyTorch/Tensorflow), Matlab, C++, version control software (e.g. git), and statistical analysis using R, SQL, etc. Familiarity with