Postdoctoral Researcher

Responsibilities

• The postdoctoral researcher will work on one or more of these aspects:
• Efficient representation - What are the most informative features to use for this task? Can we generate new ones?
• Better ML modeling - Everything about improving the machine learning modeling and making it more resilient to generalization, from new algorithms that capture relational inductive biases to domain adaptation strategies to equation discovery.
• Testing ESMs and GOBMs: Through our work on optimal representation, we also plan to develop custom metrics to assess how well the relationship between feature variables and pCO2is captured in the models, compared to the observations.
• Other related duties as assigned.
• Additional responsibilities include occasional travel (once or twice a year) to conferences and workshops to present research.
• Additional information
• This is a full time, in-person position; candidates are expected to be in the office at least three days a week.
• Application Instructions
• For full consideration, applicants should submit the following materials by November 15th, 2025:
• a Curriculum Vitae with a list of publications;
• a cover letter (no more than 2 pages) describing their research experience, available start date, career plans, and how their interests and skills would fit the project. Please also include the names of 3 references that could be contacted to request confidential letters of recommendation.
• For additional information, please contact Dr. Viviana Acquaviva at vacquaviva@citytech.cuny.edu.

Requirements

• Strong self-motivation, curiosity, a genuine interest in the topic of Climate Data Science, a collaborative mindset, and the d

Additional Information

Thank you for considering a career with the Research Foundation of The City University of New York (RFCUNY)! We are thrilled that you are interested in exploring opportunities to join our team. Primary Location: NYC COLLEGE OF TECHNOLOGY Bargaining Unit: Yes The Terra D2I (Data To Insights) lab at the CUNY New York City College of Technology, led by Dr. Viviana Acquaviva, is seeking a highly motivated postdoctoral researcher to join a growing research group for the project "From sparse data to full spatio-temporal fields: surface ocean carbon and beyond", sponsored by the Simons Foundation. Project Overview This project aims to reconstruct the global surface ocean pCO2 field, starting from observations that are extremely sparse in space and time. Because of data sparsity, the reconstruction of the full field relies on additional information that can be measured from satellites, such as the temperature and salinity of the ocean. These become the features of a machine learning model that is trained to predict pCO2using the available observations as a learning set. The predictions for the ML model are then used for "infilling" or reconstructing the full pCO2 field, which serves to estimate the global ocean carbon sink. This is a naturally difficult problem for ML methods, because there is an unsolvable distribution shift between the training domain (where observations are available) and the application domain (all other points in space and time). The project's objective is to improve this reconstruction, making it more accurate and robust. The tools that we use include classical statistics, Bayesian parameter inference, and machine learning. We collaborate with a broad community of researchers, from statisticians to physical oceanographers to climate modelers to cosmologists.

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