Rank Tenure-Track Faculty Position Department of Biomedical Informatics, Division of Bioin

Overview

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Open-Rank Tenure-Track Faculty Position Department of Biomedical Informatics, Division of Bioinformatics and Computational Biology, College of Medicine. Department:
Medicine | School Biomed Sci - Biomedical Informatics

Tenure faculty (regardless of rank) – persons with the titles of professor, associate professor, assistant professor and instructor who serve on appointments totaling 50% or more service to the university and who are eligible for tenure or who have obtained tenure. Duties and responsibilities are assigned in accordance with the workload guidelines laid out in the pattern of administration of each faculty member’s tenure initiation unit (TIU) and, as appropriate, regional campus;

obligations will include research, service and/or teaching or clinical practice. These faculty will have earned doctorate or other terminal degree in the relevant field of study or possession of equivalent experience.

Division of Bioinformatics and Computational Biology (DBCB, (Use the "Apply for this Job" box below).) in the Department of Biomedical Informatics (BMI, ) at The Ohio State University (OSU) is currently seeking applicants for tenure-track faculty at the Assistant, Associate, and Full Professor levels with research interests in bioinformatics and computational biology.

Recruited faculty will join a highly collaborative community of more than thirty investigators working in a wide variety of BMI sub-domains, including bioinformatics, artificial intelligence (AI) in digital health, biostatistics, clinical informatics, and implementation science, among others. Successful candidates will maintain an active research program in bioinformatics, collaborate with investigators across the health sciences campus, teach courses within our education programs, and mentor students.

While open to all bioinformatics research areas, we are particularly interested in one or more of the following areas:

• AI and deep learning methods (AI/DL) for multi-modal data modeling and integration, e.g., multi-omics, electronic medical records (EMRs), and/or biomedical imaging data, among others.
• AI/DL for omics-based predictive modeling, e.g., disease risk, treatment response, or adverse events.
• AI/DL methodologies, foundation models, AI agents, digital twins, cloud computing, and cyberinfrastructure, in the context of bioinformatics.
• Population and functional genomics, e.g., DNA foundation models and relevant AI/DL, pan-genome analysis, structural variant analysis, and multi-omics integration.
• Proteomics/metabolomics/microbiome, including…