Open Rank - Bioinformatics (Finance)

This position will conduct collaborative research and provide active support for ongoing institute activities, especially the TEDDY type 1 diabetes study. Opportunities exist for collaborations in other NIH-funded and privately funded type 1 diabetes research and disease prevention projects. The institute works in partnership with experts in metabolomics, microbiome/viral metagenomics, gene expression, proteomics, next generation sequencing (NGS) technologies and longitudinal repeated measure analytics. Priority consideration will be given to candidates with experience analyzing these types of high-dimensional data however, other areas of statistical application will be considered.

• Doctoral degree from an accredited institution or the highest degree appropriate in the field of specialization with a demonstrated record of achievement in teaching, academic research, and service.
• Must meet university criteria for appointment to the rank of Assistant, Associate, or Full Professor.
• For Associate/Full Professor - Normally will have produced creative work, professional writing or research in refereed and other professional journals, and be a recognized authority in the field of specialization.
• The successful candidate is expected to have experience with and thorough understanding of machine learning, cluster analysis, and other bioinformatics pipelines with demonstrated productivity and leadership in his or her field.
• Programming skills in Python, Perl, R (Bioconductor), SAS, Java, MatLab, C, C++, Bash or similar.
• Demonstrated experience in using standard GWAS and NGS analysis software such as plink, vcftools, bcftools, samtools, GATK, Picard, bedtools, bwa, tuxedo suite tools (bowtie, tophat, cufflinks, cuffmerge, cuffcompare, and cuffdiff) etc.
• Experience working with 1000 Genomes, ENCODE, or GTEx data preferred
• Familiarity with different assays, modes of sequencing, and analysis approaches (WGS, WES, mass spectrometry (proteomics/metalomics), epigenetics, metagenomics, and RNA-seq).
• Experience working in a Linux environment to develop reproducible, scalable, and shareable bioinformatics analysis pipelines.
• Familiarity with high-performance computing and grid computing systems.
• Foundational training in statistics or mathematics preferred.
• Team oriented with excellent verbal and written communications skills required.
• Experience in the design, conduct and analyses of large observational and epidemiological studies, multiple data types (genomics, metabolomics proteomics) is desirable.

The University of South Florida is a high-impact global research university dedicated to student success. Over the past 10 years, no other public university in the country has risen faster in U.S. News and World Report's national university rankings than USF. Serving more than 50,000 students on campuses in Tampa, St. Petersburg and Sarasota-Manatee, USF is designated as a Preeminent State Research University by the Florida Board of Governors, placing it in the most elite category among the state's 12 public universities. USF is a member of the American Athletic Conference.
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Working at USF
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With more than 16,000 employees at USF, the University of South Florida is one of the largest employers in the Tampa Bay region. At USF you will find opportunities to excel in a rich academic environment that fosters the development and advancement of our employees. We believe in creating a talented, engaged and driven workforce through on-going development and career opportunities. We also offer a first class benefit package that includes medical, dental and life insurance plans, retirement plan options, tuition program and generous leave programs and more.
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To learn more about working at USF please visit: Work Here. Learn Here. Grow Here .

• Design and implement analytical strategies for genome-wide association study (GWAS) data and NGS data including WGS, RNA-Seq as well as other `omics data including metabolomics, microbiome/viral metagenomics, gene expression, epigenomics, proteomics, etc. using established pipelines and workflows.
• Perform appropriate quality control (QC) measures for various omics data.
• Implement, maintain, and document data analysis pipelines and workflows.
• Benchmark new tools for high-dimensional data analysis as well as develop and apply novel analysis approaches to existing `omics data.
• Collaborate with other technical teams in specifying requirements for tools to enhance workflow efficiency and robustness.
• Work closely with epidemiologists and statisticians to conceive analytical and computational solutions that address project needs.
• Contribute to reports and papers for presentation and publication and present at scientific conferences as appropriate.
• Regularly attend and present in team meetings to share results and plan projects and experiments.
• Opportunities exist for teaching and graduate student mentoring if desired.
• Responsible to a Chair or other appropriate administrator of a State university. Responsible for teaching, research, service and related administrative activities. Responsible for academic advising. May represent the university, college/school, or department/unit on university and/or Statewide committees.