Providence, RI
Computational Biologist
Nabsys is advancing genomics with a clear focus on accessibility and innovation through its proprietary electronic genome mapping (EGM) technology. Implemented on the OhmX Platform™, EGM integrates precision electronics, nanofluidics, and computational biology to deliver high-resolution insight into genome structure. This approach expands what’s possible in cytogenetics, molecular genetics, and cell and gene therapy research, while providing comprehensive structural variation analysis to laboratories of all sizes.
This position is a key role. The successful candidate’s work will impact the design, development, efficacy, and industry acceptance of the Nabsys informatics pipeline.
- Develop, implement, and support validation of one or more methods to enhance the Nabsys analytics platform, including biophysical and statistical modeling of electronic signal data; genomic mapping and assembly methods; and variant calling and annotation procedures
- Develop focused custom methods to enable application of Nabsys technology to cancer and rare disease use cases, esp. the detection of alterations relevant to hematological cancers
- Evaluate the efficacy of current Nabsys SV calling algorithms, make comparisons with orthogonal technologies, and support publication of relevant performance metrics
- Collaborate with interdisciplinary staff to support robust operation of deployed production systems as well as development of enhancements to the core Nabsys technology
- M.S. or Ph.D. in Bioinformatics, Computational Biology, Computer Science, or related field
- 5+ years of experience applying and/or developing quantitative methods for analysis of genomic sequence analysis, including any of Illumina, PacBio, ONT, or other optical mapping data
- Excellent skills in bio-computational programming, scripting, querying, and performing statistical analysis using Python
- Solid mathematical, statistical, and analytical skills
- A pragmatic approach to problem solving
- Excellent organizational and interpersonal communication skills
- Ability to thrive in a fast-paced and challenging environment with interdisciplinary collaboration
Preference for prior experience, including:
- Experience applying quantitative methods for SV detection and visualization
- Knowledge of and experience utilizing publicly available SV databases
- Experience with optical mapping data
- Application of machine learning and AI methodology
- Programming experience in C++
- Cloud implementation and deployment experience
