"Postdoctoral Associate, Earth & Planetary Sciences"

Description

The Yale Center for Natural Carbon Capture (YCNCC) and the Yale Department of Earth & Planetary Sciences (EPS) invite applicants for a full-time Postdoctoral Researcher position to begin January 1, 2026, or no later than July 1, 2026. 

The successful candidate will help build a marine carbon dioxide removal (mCDR) forecasting system to support monitoring, reporting, and verification (MRV) for marine-based carbon dioxide removal. The project is led by Prof. Elizabeth Yankovsky, Dr. Luke Gloege, and Prof. Noah Planavsky and sponsored by the Bezos Earth Fund’s AI for Climate and Nature Grand Challenge. 

Project Overview

This project aims to develop a forecasting system for mCDR from geochemical climate interventions such as enhanced weathering and ocean alkalinity enhancement. These approaches work by increasing ocean alkalinity, allowing the ocean to draw down and store atmospheric CO2 as stable bicarbonate. However, quantifying the efficiency and permanence of this storage remains a key challenge, due to complex and unresolved ocean-atmosphere dynamics.

We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will allow users to input CDR forcing (e.g., alkalinity addition) and produce day-by-day forecasts of CO2 uptake and storage durability. The project combines physics-based modeling, machine learning, and high-performance computing to deliver fast, accurate, and accessible forecasts for monitoring, reporting, and verification (MRV) of geochemical CDR.

The overarching goal is to improve understanding and quantification of ocean carbon storage efficiency, enabling more reliable MRV and better planning and optimization of large-scale carbon removal interventions.

Key Responsibilities

The postdoctoral researcher will primarily focus on downscaling atmospheric data that will force an ocean model. The researcher will work closely with colleagues at NVIDIA using their CorrDiff tool to downscale atmospheric data over the ocean. The researcher must have knowledge of atmospheric science as well as a proven record in scientific applications of machine learning.

The postdoc will participate in co-mentoring at least one junior researcher and will have many opportunities for further professional development, decided together with the PI and according to their professional goals and interests.

Additional responsibilities include the possibility of occasional travel (once or twice a year) to conferences and workshops to present research.

Additional Information

The target start date for this position is early 2026. Postdoctoral Associates at Yale are appointed for 12-month terms. This position has funding for two years. There is the possibility of renewal subject to satisfactory performance and available funding. The salary range for this position is $75,000-105,000, commensurate with experience and skills. A separate budget for computer supplies and publication support are also available. This is a full time, in-person position; however, remote work will be considered for an exceptional candidate.

Qualifications

• Completion of a PhD in atmospheric/ocean science, computer science, physics, mathematics, or a related field at the time of the appointment
• Solid understanding of atmospheric dynamics and experience with climate modeling
• A record of relevant publications in the peer-reviewed scientific literature appropriate to their career stage
• Strong programming experience in languages such as Python, Julia, and Fortran
• Familiarity with data analysis, visualization, and statistical methods for large datasets
• Experience with version control systems (e.g., Git) and collaborative coding practices
• Ability to work in high-performance computing (HPC) environments
• Ability to work independently and as part of an interdisciplinary team in a fast-paced environment

Preferred Qualifications:

• Demonstrated experience in downscaling model outputs and/or atmospheric data
• Experience with the Oceananigans model
• Interest in the application of machine learning to scientific problems
• Interest in mCDR

Application Instructions

A complete application includes:

• Full CV, including a complete bibliography
• 3 Letters of Recommendation

Applications should be submitted via Interfolio: https://apply.interfolio.com/178791 For information regarding the Department of Earth & Planetary Sciences, visit http://earth.yale.edu. For information regarding the YCNCC, visit https://naturalcarboncapture.yale.edu/. Questions regarding the application process can be addressed to Assistant Professor Elizabeth Yankovsky (elizabeth.yankovsky@yale.edu).