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Position: Bioinformatics/Machine Learning/Biomarker Discovery Postdoctoral Fellow
Institution: Moffitt Cancer Center
Location: Tampa, Florida, United States
Duties: Develop and extend machine learning methods for big cancer Omics data such as single-cell omics data and spatial transcriptomics data; Develop bioinformatics pipelines for analyzing high dimensional immunological data; Develop novel visualization tools for interpreting big cancer Omics data; Discover new predictive biomarkers for cancer prognosis; Collaborate on a variety of cancer and bioinformatics research projects
Requirements: Research background/experiences in Bioinformatics, Statistics, Computational Biology, Machine Learning, or Computer Science; A highly motivated and independent researcher with a strong quantitative scientific background; Programming experience in statistical program R and/or scripting languages such as Perl/Python on Unix/Linux systems; Experience in cancer genomics is not required but the ability to learn new knowledge is highly desirable; Excellent communication and writing skills; Experience of integration of multi-omics data such as WES, RNA-seq, metabolomics, proteomics, etc, is preferred
   
Text: Bioinformatics/Machine Learning/Biomarker Discovery Postdoctoral Fellow Develop and extend machine learning methods for big cancer Omics data such as single-cell omics data and spatial transcriptomics data; Develop bioinformatics pipelines for analyzing high dimensional immunological data; Develop novel visualization tools for interpreting big cancer Omics data; Discover new predictive biomarkers for cancer prognosis; Collaborate on a variety of cancer and bioinformatics research projects Research background/experiences in Bioinformatics, Statistics, Computational Biology, Machine Learning, or Computer Science; A highly motivated and independent researcher with a strong quantitative scientific background; Programming experience in statistical program R and/or scripting languages such as Perl/Python on Unix/Linux systems; Experience in cancer genomics is not required but the ability to learn new knowledge is highly desirable; Excellent communication and writing skills; Experience of integration of multi-omics data such as WES, RNA-seq, metabolomics, proteomics, etc, is preferred
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