Postdoctoral Research Scientist, OAE Process-Model Testbed
Cultivarium · Boulder, CO ·
- Work mode
- Onsite
- Employment
- Contract
- Company size
- 11-50
About [C]Worthy
[C]Worthy is a non-profit research organization focused on transforming research-grade oceanographic models into open, reproducible, and auditable decision-support tools for ocean-based carbon dioxide removal (CDR). Our mission is to deliver trustworthy, science-backed tools for Monitoring, Reporting, and Verification (MRV)—enabling governments, communities, and markets to make high-stakes ocean decisions grounded in transparent, scientifically credible evidence.
We are building three interlocking open-science capabilities: C-Star, an open-source modeling platform that systematizes regional ocean model configuration, execution, and evaluation through versioned inputs, automated workflows, and comprehensive provenance tracking; the C-Star Ocean Network (C-SON), a growing constellation of standardized regional modeling domains designed to support consistent CDR accounting across sites and regions; and the Ocean CDR Atlas, an open dataset library and web tools that map CDR performance, connectivity, and risk drivers across ocean regions—providing the basis for low-cost MRV evaluation and standard-setting worldwide.
These capabilities are not CDR tools alone—they are the foundation for scalable ocean science. Robust shared infrastructure makes knowledge transferable and auditable: once validated domains, standardized workflows, and reusable tooling are in place, the marginal cost of each additional application falls and the benefits of research innovation can flow across a network of projects and regions. We aspire to democratize access to sophisticated ocean information tools, empowering diverse scientific communities to participate in ocean governance and marine resource management on their own terms.
Role Overview
This project addresses a fundamental bottleneck in OAE science: the mismatch between the complexity of the relevant biogeochemical processes and the computational cost of resolving them in full 3D ocean simulations. The 1D testbed eliminates that mismatch, enabling rapid parameter sweeps, systematic perturbed-parameter ensembles, and clean comparison against laboratory and field datasets. The work proceeds in close collaboration with industry partners including Ebb Carbon, Planetary, and Isometric, who will contribute empirical constraints from field and laboratory deployments.
Key Responsibilities
Testbed Development and Scientific Investigation
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Design and implement a one-dimensional OAE process-model testbed within C-Star, using a lightweight physical solution (idealized mixing, turbulence, lateral flows) that allows rapid model prototyping and iteration.
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Develop a generalized representation of explicitly sinking particulate material within the MARBL biogeochemical model, including parameterizations of mineral dissolution (e.g., olivine, calcite), precipitation kinetics, and particle sinking velocities.
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Develop and calibrate representations of particles generated in situ by electrochemical and mineral alkalinity addition, in collaboration with industry and other research partners.
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Run large perturbed-parameter ensembles to systematically explore parameter space and quantitatively assess uncertainty in OAE-relevant outcomes.
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Validate parameterizations against existing empirical datasets from laboratory experiments and field deployments, in close collaboration with Ebb Carbon, Planetary Technologies and other partners.
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Develop and/or implement a simplified model of alkalinity cycling in seafloor sediments, characterizing the level of sediment process representation required for robust OAE simulation at regional scales.
Integration and Infrastructure
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Implement all process formulations within the ROMS-MARBL framework so that validated parameterizations are immediately portable to three-dimensional C-Star configurations.
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Contribute to C-Star software infrastructure, ensuring the testbed is a first-class C-Star component with proper blueprint and workplan schema, documentation, and reproducibility.
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Develop tutorials and demonstrations for how to use the testbed to develop and improve OAE-related parameterizations—supporting the broader research community's ability to contribute process advances.
Scientific Communication and Collaboration
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Maintain active, substantive collaboration with Ebb Carbon, Planetary Technology, Isometric, and other partners to ensure model development is grounded in practical implementation and observational reality.
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Submit peer-reviewed manuscripts documenting parameterization calibration, validation, and scientific findings.
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Contribute to [C]Worthy's broader scientific program, including participation in field-building, user roundtables, and community engagement.
Required Qualifications
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Ph.D. in oceanography, marine chemistry, geochemistry, chemical oceanography, or a closely related field.
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Strong background in ocean biogeochemistry, including carbonate chemistry, alkalinity, and marine carbon cycling.
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Familiarity with marine mineral dissolution/precipitation kinetics and/or particle dynamics.
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Scientific software development proficiency in Python; experience working with biogeochemical model code (Python, Fortran, or Julia).
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Ability to design systematic numerical experiments, interpret results in light of observational constraints, and communicate findings clearly.
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Collaborative disposition and comfort working in partnership with industry stakeholders alongside academic researchers.
Preferred Qualifications
- Experience with MARBL or a comparable marine biogeochemistry library; familiarity with ROMS-MARBL coupling.
- Prior research on OAE, mineral weathering and dissolution, mineral precipitation, or related alkalinity-modifying processes.
- Experience developing, calibrating, or validating models.
- Laboratory or field experience with OAE experiments, tracer releases, or marine carbonate chemistry measurements.
- Familiarity with Bayesian calibration methods, ensemble parameter estimation, or uncertainty quantification.
- Interest in agentic AI applications for scientific model development and workflow automation.