UNIV - Assistant Professor, Division of Biomedical Informatics and AI - Department of PHS

Medical University of South Carolina • Charleston, SC

2d • Onsite

About The Position

The Division of Biomedical Informatics and AI in the Department of Public Health Sciences at the Medical University of South Carolina (MUSC) College of Medicine invites applications for a full-time, tenure-track or non-tenure track Assistant Professor position in Translational Bioinformatics. We seek innovative and collaborative scientists whose research leverages large-scale human genomic and health data resources to advance precision medicine and improve human health. Areas of interest include, but are not limited to, the analysis of electronic health record (EHR)-linked biobanks, human genetics and genomics, statistical genetics, genetic epidemiology, genome-wide and phenome-wide association studies (GWAS/PheWAS), pharmacogenomics, genomic medicine, and the development and application of artificial intelligence (AI) and machine learning (ML) methods for genomic and clinical data. Successful candidates will establish impactful research programs focused on translating genomic discoveries into biological, clinical, and population health insights. Candidates with experience integrating multimodal data sources, including genomic, transcriptomic, sequencing, clinical, imaging, and other real-world data, are especially encouraged to apply. Faculty track will be commensurate with qualifications and experience.

Requirements

• PhD, MD, MD/PhD, or equivalent degree in biomedical informatics, computational biology, genetics/genomics, biostatistics, computer science, genetic epidemiology, or a related field.
• Demonstrated expertise in large-scale human genetic and genomic analyses, including GWAS, PheWAS, sequencing studies, and/or pharmacogenomics.
• Experience working with EHR-linked biobanks and real-world clinical data.
• Evidence of developing or applying AI/ML methods to genomic, sequencing, multimodal, or healthcare data.
• Demonstrated research productivity and potential for securing independent funding.
• Commitment to excellence in teaching, mentoring, and interdisciplinary collaboration.

Nice To Haves

• Candidates with experience integrating multimodal data sources, including genomic, transcriptomic, sequencing, clinical, imaging, and other real-world data, are especially encouraged to apply.

Responsibilities

• Develop and maintain an independent, nationally recognized, externally funded research program in translational bioinformatics, human genetics, genomics, precision medicine, and/or AI-enabled biomedical discovery.
• Conduct innovative research leveraging EHR-linked biobanks and large-scale genomic and clinical datasets to investigate disease risk, therapeutic response, and health outcomes.
• Develop and apply advanced statistical, computational, and AI/ML methods for the analysis of genomic, sequencing, phenotypic, and health-related data.
• Lead and collaborate on studies involving GWAS, PheWAS, fine-mapping, polygenic risk modeling, pharmacogenomics, genetic epidemiology, genomic medicine, and related areas.
• Collaborate with investigators across MUSC, including clinical, translational, biomedical, public health, and data science researchers, to advance interdisciplinary research initiatives.
• Contribute to institutional efforts supporting precision medicine, biobank science, genomics, and AI-driven healthcare innovation.
• Teach and mentor students, fellows, and trainees in biomedical informatics, data science, genetics, genomics, and related graduate education programs.
• Participate in the training and mentoring of the next generation of researchers through interdisciplinary research, educational, and workforce development initiatives.
• Contribute to service activities within the Division, Department, College, University, and the broader scientific community.

Benefits

• Competitive start-up package, including dedicated research space, access to core facilities, and administrative support for grant development.
• Mentorship
• Interdisciplinary collaboration
• Integration with clinical and translational research initiatives

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree