Description
An exceptional opportunity has been announced for a Postdoctoral Fellow to join the laboratory of Dr. Kai Wang at the Department of Environmental and Occupational Health, Indiana University School of Public Health, Bloomington. This fellowship is intended for highly motivated researchers eager to contribute to impactful scientific advancements in the field of environmental health. The position will be focused on computational toxicology, bioinformatics, and the analysis of large-scale omics datasets. Candidates will be expected to employ advanced computational and statistical methods to interpret complex biological data. Special emphasis will be placed on investigating how environmental exposures influence human health at the molecular and systems levels. The role will provide access to state-of-the-art computational facilities and extensive environmental health datasets. Collaboration with interdisciplinary teams, including toxicologists, epidemiologists, and computational scientists, will be strongly encouraged. The fellowship will also offer opportunities for professional development, grant writing, and participation in high-impact publications. Successful applicants will demonstrate a strong background in bioinformatics and programming while showing the ability to work independently and collaboratively. This position represents a remarkable chance for early-career scientists to expand their expertise while contributing to meaningful discoveries in environmental health research.
Responsibilities
Key Responsibilities
The Postdoctoral Fellow will play a vital role in advancing computational toxicology and environmental health research. A primary focus will be placed on handling and interpreting large-scale omics datasets derived from diverse studies. Innovative bioinformatics pipelines and machine learning approaches will be developed to reveal complex exposure–disease links. The fellow will contribute to impactful scholarship by publishing findings in high-quality journals and presenting at global conferences. Collaboration with interdisciplinary research teams will be fostered to ensure comprehensive and translational outcomes.
- Large-scale omics datasets from environmental health studies will be processed, integrated, and interpreted.
- Novel bioinformatics pipelines and machine learning models will be created and applied to investigate exposure–disease associations.
- Research outcomes will be communicated through peer-reviewed publications and international conferences.
- Active collaboration will be maintained with interdisciplinary teams, including toxicologists, epidemiologists, and computational scientists.
Why Join Indiana University?
Indiana University Bloomington offers a stimulating research environment for advancing careers in computational toxicology and bioinformatics. Fellows will benefit from exclusive access to large-scale datasets and cutting-edge computational infrastructure. The university fosters a highly collaborative and interdisciplinary culture, encouraging cross-disciplinary innovation. Opportunities for career development, grant writing, and structured mentorship are integral to the fellowship experience. This position provides a platform to contribute to impactful research while preparing for long-term academic and professional success.
- Access will be provided to large-scale datasets and state-of-the-art computational resources.
- The position offers an opportunity to engage in a collaborative and interdisciplinary environment at Indiana University Bloomington.
- Training in career development, grant writing, and mentorship will be encouraged.
Qualification
Required Qualifications
To be considered for this postdoctoral fellowship, applicants must demonstrate strong academic and technical foundations relevant to computational toxicology and bioinformatics. A completed Ph.D. in bioinformatics, computational biology, toxicology, environmental health, biostatistics, computer science, or a related discipline is essential. Expertise in programming languages such as R, Python, or equivalent is expected, along with prior exposure to high-performance computing environments. Candidates should possess hands-on experience with omics datasets, including genomics, transcriptomics, or epigenomics. Familiarity with toxicological databases and analytical pipelines will be regarded as a significant advantage. Applicants are required to demonstrate strong analytical and problem-solving skills, with the ability to design and implement novel computational approaches. The fellowship values independence in researching while maintaining active engagement in collaborative projects. Ideal candidates will show commitment to producing impactful scientific outcomes and contributing to interdisciplinary teamwork.
- A Ph.D. in bioinformatics, computational biology, toxicology, environmental health, biostatistics, computer science, or a related discipline.
- Demonstrated proficiency in programming languages such as R, Python, or equivalent, along with experience in high-performance computing.
- Prior research experience with omics datasets (genomics, transcriptomics, epigenomics) and knowledge of toxicological resources or databases.
- Strong problem-solving skills, independence in research, and the ability to function within collaborative teams.
Application Procedure
Applications should include a curriculum vitae, a cover letter, and the contact details of three references. Submissions must be made through the official application portal: Apply Here.
For additional inquiries, Dr. Kai Wang(at kwa2@iu.edu) may be contacted directly. Applications will be reviewed on a rolling basis until the position is successfully filled.
This fellowship offers an excellent platform for early-career researchers passionate about environmental health, toxicology, and computational biology to advance their academic careers while making meaningful contributions to scientific discovery. Indiana University is firmly committed to being an equal opportunity employer, fostering diversity, equity, and inclusion within its academic community.
