The Department of Biomedical Informatics (BMI) currently has multiple opportunities for tenure-track faculty with research interests related to biomedical informatics, including clinical informatics, research informatics, public health informatics and translational bioinformatics.
As part of the College of Medicine at the Ohio State Wexner Medical Center (OSUWMC), the Department of BMI is the academic home for informatics research, development and training at Ohio State. Faculty interests span multiple domains: high-performance computing, bioinformatics, computational biology, translational bioinformatics, clinical informatics, and health outcome research. BMI faculty have ongoing collaborations with faculty in departments throughout the university and nationally.
The Ohio State University is one of the nation’s largest integrated health sciences campuses, with access to a CTSA-funded Center for Clinical and Translational Science and a state-of-the-art 1 million square foot, NCI-designated Comprehensive Cancer Center. Faculty and staff in BMI can leverage an advanced information systems environment including inpatient and outpatient electronic health records, data warehousing platforms, and a variety of enterprise research information systems. Through a partnership with the Ohio Supercomputer Center (OSC), BMI faculty have access to high-performance computing and data management facilities for both clinical and bio-molecular data.
Columbus is the state capital and the most populous city in the state of Ohio. It is the 14th-most populous city in the United States and one of the fastest growing cities in the nation.
We are specifically seeking candidates with experience and interests in one or more of the following areas:
- The discovery, collection, integration, storage, and retrieval of complex data sets in support of clinical and translational research programs;
- The translation of informatics discoveries into clinical practice through collaboration with IT;
- The application of network analysis, hypothesis discovery, natural language processing tools, and data mining methods to heterogeneous biomedical data sets;
- Risk modeling and profiling in diverse disease states and patient cohorts;
- The use of semantic web technologies in biomedical application domains;
- The use of natural language process and text mining tools to extract biological knowledge from domain literature;
- Visualization of multi-dimensional data sets;
- The analysis of linkages between bio-molecular and clinical data sets and models in order to inform hypothesis generation and in-silico knowledge synthesis.
The successful applicant will have an MD and/or PhD in biomedical informatics, computer science, computational biology, or a related discipline. To apply, please email a cover letter and a full CV (including publications and funding records); detailed plans for research and educational programs; the names of three references; and two representative publications to: BMI.firstname.lastname@example.org. PDF submissions are preferred. Evaluation of applications is underway and will continue until positions are filled.
Summer Internship Program
The Department of Biomedical Informatics offers a summer internship program annually that provides graduating high school seniors and current undergraduate and graduate students the opportunity to pursue research projects under the guidance of research and operational staff and renowned faculty mentors.
Open-rank faculty recruitment in systems immunology
The Department of BMI and the Institute for Immuno-Oncology (IIO) at the OSU Comprehensive Cancer Center jointly recruit faculty members in systems immunology.
Launched with a $100 million investment in 2019, the IIO will focus on developing strategies to harness the power of the immune system to fight cancer. One of the key areas of the IIO will be systems immunology, which utilizes omics strategy to probe the immune system, analyze changes of the tumor microenvironment in response to immunotherapy, and develop new immunological paradigms. Areas of interest include, but are not limited to, neoantigen discovery, antigen receptor repertoire analysis, microbiome, single cell genomics and transcriptomics and functional genomics.
Applications are encouraged from cancer immunologists, physician scientists, and bioinformaticians with interests in any of these aforementioned areas in immune-oncology. Previous use of OMICs including transcriptomics, proteomics, epigenomics, microbiome and metabolome, and expertise in big data science are essential. The positions will be supported by substantial start-up packages. Candidates at the assistant professor level should have a demonstrated record of success in multiple venues with substantial potential to obtain extramural funding. Candidates at the associate or full professor levels should have robust and active research programs and demonstrable national or international reputations, respectively.
Interested candidates should forward a cover letter, CV and statement of research interests to BMI.email@example.com. PDF submissions are preferred. Evaluation of applications is underway and will continue until positions are filled.
The IIO is a newly established Institute in The Ohio State University Comprehensive Cancer Center (OSUCCC) – James, where Dr. Zihai Li is the founding director. The OSUCCC – James is a National Cancer Institute designated Comprehensive Cancer Center and a founding member of the National Comprehensive Cancer Network, an alliance of the nation’s leading cancer centers that develops and institutes standards of care for the treatment and control of cancer, the advancement of biomedical knowledge through laboratory and clinical research, and the training of scientists, physicians and other health care workers.
For staff positions, including research, postdoctoral, and administrative positions, please see the Ohio State Wexner Medical Center & College of Medicine Careers section.
Openings of Postdoctoral Scholar Positions at the Au Lab
Postdoctoral scholar in bioinformatics method development
Postdoctoral scholar in computational biology