Institution: Columbia University, New York, NY
Contact: Dr. Xiang-Jun Lu ([email protected])
Official Application: https://apply.interfolio.com/183705
Description: The NIH-funded X3DNA-DSSR National Resource is seeking a highly motivated researcher to join our team at Columbia University. X3DNA-DSSR is a leading tool in structural bioinformatics, providing the geometric “ground truth” for the dissection and modeling of 3D nucleic acid structures.
Our resource is a primary contributor to the scientific community’s visual landscape. In 2025, 10 out of 12 cover images for the journal RNA were contributed by X3DNA-DSSR, a trend that continues with the February 2026 and March 2026 covers. To see our resource and visualizations in action, please visit wDSSR: https://web.x3dna-dssr.org/.
The Role: While the administrative rank for this position is Staff Associate II, the role offers significant intellectual freedom to pursue publication-oriented research. We are looking for a candidate with a strong scientific background in structural biology or bioinformatics and a desire to contribute to peer-reviewed publications through community-driven data analysis. The successful candidate will lead research in specialized areas—such as G-quadruplex analysis, i-motifs, and complex motif characterization—and is encouraged to develop independent research interests within the DSSR framework.
Key Opportunities:
- Lead and co-author high-impact scientific publications.
- Master high-end scientific visualization (as seen on the covers of the journal RNA).
- Flexibility to modernize and evolve the next-generation DSSR analysis engine.
- Engage with and support a global community of RNA researchers.
Required Skills: The ideal candidate should possess strong programming skills (e.g., Python, Ruby, bash, R, Java, or C/C++) and experience working in Linux/Unix/macOS environments. Familiarity with version control systems (e.g., Git) is required, along with a demonstrated interest in scientific software development. Experience applying machine learning techniques (such as Graph ML or Transformers) to biological datasets is a significant plus, as we look to integrate AI-driven insights into the DSSR framework.
Inquiries & Application: Informal inquiries are welcome and can be sent directly to Dr. Xiang-Jun Lu at [email protected].
To submit a formal application, please visit the official Columbia University posting: https://apply.interfolio.com/183705. If you apply via the official link, please also send a brief notification email to Dr. Lu at the email address above to ensure your application is tracked personally.
