University of Iowa


Iowa City

 United States

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131 days ago Apply now


The Division of Biostatistics and Computational Biology offers multidisciplinary work environment and a highly collaborative culture. The research projects include developing computational pipelines/algorithms for microbiome omics data integration and analysis. We develop and apply bioinformatics approaches to forge quantitative links between genetic, taxonomic, and functional aspects of microbial diversity and activities. Other research opportunities include bioinformatics development for oral cancer omics data integration and analysis to discover cancer genetic susceptibility. We develop and apply computational systems biology approaches to investigate the landscapes of genetic content in human oral cancer to identify molecular biomarkers for oral squamous cell carcinoma by mining and integrating patterns discovered from combined large data of bulk RNA-seq, single-cell RNA-seq, and epigenomics data. Other projects include biological ontology mining and network analysis for genotype-phenotype associations and electronic health record data mining on integrated dental patient data and general hospital data to improve decision making and personalized treatment, etc. The successful applicant will be under the supervision of Dr. Erliang Zeng, Associate Professor whose research is primarily in bioinformatics and data science, and Dr. Xian Jin Xie, Division Director and the Association Dean for Research for the College of Dentistry, whose research is primarily in biostatistics and bioinformatics. Both Drs. Xie and Zeng are affiliated faculty in the Department of Biostatistics of College of Public Health and the multidisciplinary Informatics Program.


data analysis and interpretation,

presentation at professional meetings,

manuscript preparation, and

grant writing.


PhD in computer science, bioinformatics, data science, biostatistics, or related field.

Research experience in areas of bioinformatics, omics data mining, computational systems biology, biological big data mining, or comparative genomics.

Extensive programming skills for biological big data mining using machine learning and AI approaches.

Prior experience with large next generation sequencing (NGS) omics data in one or more of the following: bulk RNA-seq, single-cell RNA-seq, ATAC-seq, CHIP-seq, and Methylation-seq.

Strong verbal and written communication skills.

Self-motivation skills with the ability to work independently and in a group environment.


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