Overview As part of the Computational Team, you will build analytical foundations connecting nanopore measurements to biological insight. You will work across biology, physics, signal processing, and machine learning to transform complex electronic data into actionable sequencing outputs. The role blends experimental collaboration with method development in an open-ended problem space. You will shape how noisy measurements are interpreted and scaled across the platform, contributing to a high-impact, frontier technology. This is a chance to help define new computational approaches at the edge of current knowledge and drive meaningful scientific discovery.
Compensation / Benefits- salary range $115,000 - $165,000
- healthcare coverage
- annual incentive program
- retirement benefits
- broad range of other benefits
Responsibilities- Analyze nanopore and electrophysiology datasets to identify biologically meaningful signal features.
- Develop and implement computational workflows for event detection, classification, and molecular fingerprinting.
- Build quantitative models that connect nanopore measurements to molecular identity, structure, and function.
- Collaborate with experimental scientists to interpret results and shape future experiments.
- Partner with protein science, signal processing, and machine learning teams to improve platform performance.
- Help establish best practices for benchmarking, validation, and uncertainty quantification as the platform scales.
Key requirements- Strong programming skills in Python and scientific computing environments.
- Experience analyzing complex biological, physical, or time-series datasets with noise and experimental variability.
- Solid background in at least two of: statistics, machine learning, signal processing, or Bayesian modeling.
- Comfortable working across experimental and computational domains with wet-lab scientists on data interpretation.
- Clear scientific communicator - written and verbal.
- cross-disciplinary collaboration
- clear scientific communication
- adaptability
- Python
- time-series analysis
- statistics
