Staff Scientist, Modeling Applications
Cultivarium · Boulder, CO · onsite
USD 125,000 to 160,000 · period not stated
Role Overview:
- Answer central scientific questions at the frontier of ocean-based CDR research
- Develop and use cutting edge open-source ocean modeling tools, paving the way for others to follow your steps
- Leverage AI & ML to expand impact and enhance tooling for CDR
- Design and deploy data visualizations of large geospatial datasets
- Interact with academic, government, and industry researchers and practitioners of ocean-based CDR
- Contribute to collaborative open-source software packages
- The successful candidate will be a technically well-rounded ocean modeler who can contribute across the full modeling lifecycle—from model and scientific software development to model application, analysis, and interpretation. The ideal candidate will be comfortable working collaboratively as a member of a larger technical team, moving across a variety of projects, learning new tools and scientific domains, and helping expand our capabilities into new areas of oceanographic modeling.
Required Qualifications:
- Ph.D. in oceanography, atmospheric science, geophysical fluid dynamics, marine biogeochemistry, or a closely related field
- Demonstrated experience configuring, running, and evaluating global/regional ocean and/or estuary models (e.g., Oceananigans, ROMS, MOM6, NEMO, FVCOM, SCHISM, MITGCM, Delft3D, or equivalent)
- Working knowledge of ocean biogeochemistry, including carbonate chemistry and air-sea gas exchange
- Proficiency in Python
- Strong written and oral communication skills, including the ability to convey technical results to non-specialist audiences; ability to collaborate in a small, fast-moving research team
Preferred Qualifications:
- Interest in and familiarity with AI/ML approaches applied to scientific modeling or data analysis.
- Prior work on CDR quantification, MRV methodology, or ocean carbon cycle research
- Experience developing reproducible scientific workflows
- Familiarity with cloud-native, multi-dimensional data formats (e.g., ZARR, HDF, netCDF, COG), visualization tools, and public dataset publication practices
- Experience with HPC computing environments (e.g., Slurm)
- Familiarity with uncertainty quantification, ensemble methods, or Bayesian calibration