Postdoctoral Scholar Research

University of South Florida • Tampa, FL

7d

About The Position

The Health Informatics Institute (HII) at the University of South Florida is excited to announce multiple postdoctoral positions, available immediately. We are seeking highly motivated candidates with strong backgrounds in Statistical Genetics, Bioinformatics, Data Science, Genetic Epidemiology or related fields to join our team. The successful candidates will work on NIH-funded projects aiming to understand disease mechanisms and identify biomarkers for Alzheimer's Disease, type 1 and type 2 diabetes, and cardiovascular diseases. Our research utilizes high-dimensional omics data, including genomic, epigenomic, transcriptomic, lipidomic, metabolomic, and metagenomic data, from large human populations. The successful candidates will lead research projects, conducting integrated multi-omics analysis using state-of-the-art statistical, bioinformatic, and machine learning approaches to uncover disease mechanisms and molecular pathways associated with these complex disorders. Successful candidates will also engage in close collaboration with a diverse team of interdisciplinary experts, including faculty from the Health Informatics Institute (HII) at the University of South Florida, and the Moffitt Cancer Center.

Requirements

• Doctoral degree from an accredited institution and appropriate experience and training within a selected area of specialization.
• Must meet university criteria for appointment to the rank of Postdoctoral Fellow.

Nice To Haves

• Ph.D. in Bioinformatics, Biostatistics/Statistics, Statistical Genetics, Genetic Epidemiology or related fields.
• Strong record of peer-reviewed publications.
• Excellent written and verbal communication skills.
• Proficiency in shell scripting and programming (R, Python, C, C++, or Java) within UNIX/Linux environments, especially for handling large, diverse data files.
• Experience analyzing human sequence data using bioinformatics tools (e.g., SAMtools, GATK, Picard) and genetic databases (e.g., Ensembl Genes, 1000 Genomes, ESP, HapMap).
• While not required, prior experience in multi-omics data analysis is highly desirable

Responsibilities

• Analyze high-throughput omics data (e.g., GWAS, WGS, RNA-seq, methylation, lipidomics, microbiome, single-cell).
• Develop and implement novel statistical and machine learning methods for multi-omics integration.
• Prepare manuscripts for peer-reviewed journals, assist with grant writing, develop independent grant proposals, and deliver presentations at scientific conferences.
• Collaborate within interdisciplinary teams and work independently.

What This Job Offers

• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees