Hiring: Scientist, Computational Sensing Company: Flagship Pioneering Location: Cambridge, MA Job Posted Time: 2026-09-02 17:58:09 Employment Type: Full-time Target Skills & Keywords: Nanopore biophysics, electrophysiology, signal processing, machine learning, synthetic biology, protein engineering, bioinformatics, Python, scientific computing, event detection, classification, molecular fingerprinting, quantitative modeling, statistics, Bayesian modeling, time-series analysis, benchmarking, validation, uncertainty quantification Experience: - Master's degree plus 3+ years of relevant industry experience, or PhD in Computational Biology, Biophysics, Bioengineering, Physics, Applied Mathematics, Computer Science, Electrical Engineering, or a related quantitative discipline - Experience analyzing complex biological, physical, or time-series datasets, particularly in the presence of noise and experimental variability Required Skills: - Strong programming skills in Python and scientific computing environments - Solid background in at least two of: statistics, machine learning, signal processing, or Bayesian modeling - Ability to 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 connecting nanopore measurements to molecular identity, structure, and function - Comfortable working across experimental and computational domains; able to engage meaningfully with wet-lab scientists on data interpretation - Clear scientific communicator — written and verbal Qualifications: - Master's degree plus 3+ years of relevant industry experience, or PhD in Computational Biology, Biophysics, Bioengineering, Physics, Applied Mathematics, Computer Science, Electrical Engineering, or a related quantitative discipline - Strong programming skills in Python and scientific computing environments - Experience analyzing complex biological, physical, or time-series datasets — particularly in the presence of 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; able to engage meaningfully with wet-lab scientists on data interpretation - Clear scientific communicator — written and verbal Compensation: Not specified Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don’t miss this opportunity to join a forward-thinking team!