Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral scientist or more senior research scientist for research and development activities focused on data science and engineering.

The scientist will collaborate with Princeton and GFDL researchers to advance computational workflows for quality assurance and service delivery, with an emphasis on the Seamless System for Prediction and Earth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize the use of AI- and LLM-enabled tools to support automation, triage, and scalable service deliveryâ”such as identifying outliers, summarizing pre-computed diagnostics, and supporting user-facing workflows. A successful candidate will work across different aspects of the quality assurance and data engineering pipeline, developing robust, reusable, and trustworthy software solutions that enable sustainable online tools and services relevant to a broad range of stakeholders. The selected candidate will join a vigorous research group at Princeton University and work in close collaboration with the data science group at GFDL, as well as with representatives of the Seasonal-to-Decadal Variability & Predictability Division and the Technical Services and Modeling Systems Division, with access to state-of-the-art numerical models and high-performance computing systems at Princeton and NOAA.

Candidates must have a PhD in computer science, Earth science, or a closely related field. The following attributes are desirable: (a) excellent problem-solving skills and self-motivation; (b) passion for software and science; (c) strong open-source software development experience; (d) excellent written and oral communication skills; and (e) strong data analysis skills. Ideal applicants will also have experience with some combination of: (a) artificial intelligence tools ecosystems; (b) machine learning; (c) analysis optimization; (d) data visualization; (e) data compression and validation techniques; and (f) NetCDF and Zarr data models. Experience with programming languages such as Python and Julia is expected. This role emphasizes the use of artificial intelligence and large language models for workflow automation, quality control, data validation, and knowledge delivery (e.g., flagging outliers, summarizing diagnostics, and supporting user-facing services).

The Term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community. The work location for this position is in-person on campus at Princeton University. This position is subject to the University's background check policy. This position is not eligible for sponsorship of an H-1B visa requiring consular processing; other visa sponsorships (including H-1B visas not requiring consular processing) may be available, as appropriate.

Complete applications should include a cover letter, CV, publication and presentation list, 3 letters of recommendation, and a one-to-two page personal statement and/or a research statement. Review of applications will begin March 31, 2026 and continue until the position is filled. For additional information, contact Aparna Radhakrishnan (aparna.radhakrishnan@princeton.edu).