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, Organization of field trips, data collection and lab work, Spectral data analysis, data processing, and model development, R’ or Python programming, Co-supervise PhD and undergraduate students. Be willing to be
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, R’ or Python programming, Co-supervise PhD and undergraduate students. Be willing to be involved in other research activities different than spectroscopy, such as soil phosphorus, agronomy
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models, statistical analysis). Proficiency in programming languages (e.g., R, Python, MATLAB) and familiarity with relevant air quality modeling tools. Experience with large datasets and data processing
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multispectral/hyperspectral data processing. Proficiency in programming languages such as Python or R for data analysis and processing. Excellent communication skills and the ability to work effectively in a
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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with cell culture techniques. R, Python, or other data science tools for microbiome analysis. Publication record in peer-reviewed journals. Preferred Qualifications Experience with colorectal cancer
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environmental data analysis. Familiarity with theoretical and practical aspects of scientific deep learning; Proficiency in programming languages such as Python and R. Strong experience working with climate and
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, environmental sciences, geoinformatics, or a related discipline. Proficiency in advanced learning techniques and statistical modeling. Strong programming skills in languages like Python or R. Professional
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advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux
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bioinformatics tools for microbiome analysis (QIIME2, Kraken2, MetaPhlAn) and AMR gene identification (MEGARes, CARD, ResFinder). Proficiency in Python, R, or Perl, with experience in Linux/Unix environments